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How Curved Terahertz Waves Could Revolutionize Wireless Communication

By Brown University April 12, 2024

A breakthrough in wireless communication shows potential for terahertz waves to curve around obstacles, enhancing connectivity and paving the way for advanced network technologies. Credit: SciTechDaily.com

In a breakthrough that could help revolutionize wireless communication, researchers unveiled a novel method for manipulating terahertz waves, allowing them to curve around obstacles instead of being blocked by them.

While cellular networks and Wi-Fi systems are more advanced than ever, they are also quickly reaching their bandwidth limits. Scientists know that in the near future they’ll need to transition to much higher communication frequencies than what current systems rely on, but before that can happen there are a number of — quite literal — obstacles standing in the way.

Researchers from Brown University and Rice University say they’ve advanced one step closer to getting around these solid obstacles, like walls, furniture, and even people — and they do it by curving light.

Advancements in Terahertz Communication

In a new study published in Communications Engineering , the researchers describe how they are helping address one of the biggest logjams emerging in wireless communication. Current systems rely on microwave radiation to carry data, but it’s become clear that the future standard for transmitting data will make use of terahertz waves, which have as much as 100 times the data-carrying capacity of microwaves. One longstanding issue has been that, unlike microwaves, terahertz signals can be blocked by most solid objects, making a direct line of sight between transmitter and receiver a logistical requirement.

A study that could help revolutionize wireless communication introduces a novel method to curve terahertz signals around an obstacle. Credit: Illustration provided by the Mittleman Group, edited

“Most people probably use a Wi-Fi base station that fills the room with wireless signals,” said Daniel Mittleman, a professor in Brown’s School of Engineering and senior author of the study. “No matter where they move, they maintain the link. At the higher frequencies that we’re talking about here, you won’t be able to do that anymore. Instead, it’s going to be a directional beam. If you move around, that beam is going to have to follow you in order to maintain the link, and if you move outside of the beam or something blocks that link, then you’re not getting any signal.”

The researchers circumvented this by creating a terahertz signal that follows a curved trajectory around an obstacle, instead of being blocked by it.

“This is the world’s first curved data link, a critical milestone in realizing the 6G vision of high data rate and high reliability,” said Edward Knightly, a co-author on the study and professor of electrical and computer engineering at Rice University.

The novel method unveiled in the study could help revolutionize wireless communication and highlights the future feasibility of wireless data networks that run on terahertz frequencies, according to the researchers.

“We want more data per second,” Mittleman said. “If you want to do that, you need more bandwidth, and that bandwidth simply doesn’t exist using conventional frequency bands.”

Novel Techniques for Signal Transmission

In the study, Mittleman and his colleagues introduce the concept of self-accelerating beams. The beams are special configurations of electromagnetic waves that naturally bend or curve to one side as they move through space. The beams have been studied at optical frequencies but are now explored for terahertz communication.

The researchers used this idea as a jumping off point. They engineered transmitters with carefully designed patterns so that the system can manipulate the strength, intensity and timing of the electromagnetic waves that are produced. With this ability to manipulate the light, the researchers make the waves work together more effectively to maintain the signal when a solid object blocks a portion of the beam. Essentially, the light beam adjusts to the blockage by shuffling data along the patterns the researchers engineered into the transmitter. When one pattern is blocked, the data transfers to the next one, and then the next one if that is blocked. This keeps the signal link fully intact. Without this level of control, when the beam is blocked, the system can’t make any adjustments, so no signal gets through.

This effectively makes the signal bend around objects as long as the transmitter is not completely blocked. If it is completely blocked, another way of getting the data to the receiver will be needed.

“Curving a beam doesn’t solve all possible blockage problems, but what it does is solve some of them and it solves them in a way that’s better than what others have tried,” said Hichem Guerboukha, who led the study as a postdoctoral researcher at Brown and is now an assistant professor at the University of Missouri – Kansas City.

The researchers validated their findings through extensive simulations and experiments navigating around obstacles to maintain communication links with high reliability and integrity. The work builds on a previous study from the team that showed terahertz data links can be bounced off walls in a room without dropping too much data.

Practical Applications and Ongoing Research

By using these curved beams, the researchers hope to one day make wireless networks more reliable, even in crowded or obstructed environments. This could lead to faster and more stable internet connections in places like offices or cities where obstacles are common. Before getting to that point, however, there’s much more basic research to be done and plenty of challenges to overcome as terahertz communication technology is still in its infancy.

“One of the key questions that everybody asks us is how much can you curve and how far away,” Mittleman said. “We’ve done rough estimations of these things, but we haven’t really quantified it yet, so we hope to map it out.”

Reference: “Curving THz wireless data links around obstacles” by Hichem Guerboukha, Bin Zhao, Zhaoji Fang, Edward Knightly and Daniel M. Mittleman, 30 March 2024, Communications Engineering . DOI: 10.1038/s44172-024-00206-3

The work was supported by the National Science Foundation and the Air Force Office of Scientific Research.

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New artificial enzyme shows potential for new renewable energy source, 2 comments on "how curved terahertz waves could revolutionize wireless communication".

One more that supposedly intent to break the physic laws! They always come like “a new study unveil”… They claims bending terahertz frequency for wifi purposes but these frequency are between infrared and visible light and can’t be bended or curved without a huge gravity like a solar mass and its huge radius

Einstein is turning in his grave!

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Advances, Systems and Applications

• Open access
• Published: 10 April 2020

Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

• Huaming Wu 1 ,
• Xiangyi Li 1 &
• Yingjun Deng 1  

Journal of Cloud Computing volume  9 , Article number:  21 ( 2020 ) Cite this article

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Future wireless communications are becoming increasingly complex with different radio access technologies, transmission backhauls, and network slices, and they play an important role in the emerging edge computing paradigm, which aims to reduce the wireless transmission latency between end-users and edge clouds. Deep learning techniques, which have already demonstrated overwhelming advantages in a wide range of internet of things (IoT) applications, show significant promise for solving such complicated real-world scenarios. Although the convergence of radio access networks and deep learning is still in the preliminary exploration stage, it has already attracted tremendous concern from both academia and industry. To address emerging theoretical and practical issues, ranging from basic concepts to research directions in future wireless networking applications and architectures, this paper mainly reviews the latest research progress and major technological deployment of deep learning in the development of wireless communications. We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.

Introduction

Along with the incredible growth of mobile data generated in internet of things (IoT) and the explosion of complicated wireless applications, e.g., virtual reality (VR) and augmented reality (AR), the fifth-generation (5G) technology demonstrates high-dimensional, high-capacity and high-density characteristics [ 1 , 2 ]. Moreover, future wireless communication systems will become ever-more demanding for edge-cloud computing since the edge servers are in proximity of the IoT devices and communicate with them via different wireless communication technologies [ 3 , 4 ]. The requirements of high bandwidth and low latency for wireless communications have posed enormous challenges to the design, configuration, and optimization of next-generation networks (NGN) [ 5 , 6 ]. In the meantime, massive multiple-input multiple-output (MIMO) is widely regarded as a major technology for future wireless communication systems. In order to improve the quality of wireless signal transmission, the system uses multiple antennas as multiple transmitters at the base station (BS) and receivers at a user equipment (UE) to realize the multipath transmitting, which can double the channel capacity without increasing spectrum resources or antenna transmit power. However, conventional communication systems and theories exhibit inherent limitations in the utilization of system structure information and the processing of big data. Therefore, it is urgent to establish new communication models, develop more effective solutions to address such complicated scenarios and further fulfill the requirements of future wireless communication systems, e.g., beyond the fifth-generation (B5G) networks.

Along with the fast convergence of communication and computing in popular paradigms of edge computing and cloud computing [ 7 , 8 ], intelligent communication is considered to be one of the mainstream directions for the extensive development of future 5G and beyond wireless networks, since it can optimize wireless communication systems performance. In addition, with tremendous progress in artificial intelligence (AI) technology, it offers alternative options for addressing these challenges and replacing the design concepts of conventional wireless communications. Deep learning (DL) is playing an increasingly crucial role in the field of wireless communications due to its high efficiency in dealing with tremendous complex calculations, and is regarded as one of the effective tools for dealing with communication issues. Although deep learning has performed well in some IoT applications, “no free lunch” theorem [ 9 ] shows that a model cannot solve all problems once and for all, and we cannot learn a general model for a wide range of communication scenarios. This means that for any particular mobile and wireless network issue, we still need to adopt different deep learning architectures such as convolutional neural networks (CNN), deep neural networks (DNN) and recurrent neural networks (RNN), in order to achieve better performance of the communication systems.

As a classic model of deep learning, autoencoder is widely used in the design paradigms of communication system models. Autoencoder-based wireless communication models are drawing more and more attention [ 10 , 11 , 12 ]. Generative adversarial network (GAN) [ 13 ] is a promising technique, which has attracted great attention in the field of mobile and wireless networking. The architecture of GAN is composed of two networks, i.e., a discriminative model and a generative model, in which a discriminator D is trained to distinguish the real and fake samples, while the generator G is trained to fool the discriminator D with generated samples. The feature of GAN is very appropriate for training. GAN-driven models and algorithms can facilitate the development of next-generation wireless networks, especially coping with the growth in volumes of communication and computation for emerging IoT applications. However, the incorporation of AI technology in the field of wireless communications is still in its early stages, and learning-driven algorithms in mobile wireless systems are immature and inefficient. More endeavors are required to bridge the gap between deep learning and wireless communication research, e.g., customize GAN techniques for network analytics and diagnosis and wireless resource management in heterogeneous mobile environments [ 14 ].

This survey explores the crossovers and the integration of wireless communication and AI technology, aims at solving specific issues in the mobile networking domain, and greatly improve the performance of wireless communication systems. We gather, investigate and analyze latest research works in emerging deep learning methods for processing and transferring data in the field of wireless communications or related scenarios, including strengths and weaknesses. The main focus is on how to customize deep learning for mobile network applications from three perspectives: mobile data generation, end-to-end wireless communications and network traffic control that adapts to dynamic mobile network environments. Several potential deep learning-driven underlying communication technologies are described, which will promote the further development of future wireless communications.

The rest of this paper is organized as follows: we first draw an overall picture of the latest literature on deep learning technologies in the field of wireless communications. Then, we present important open issues and main challenges faced by researchers for intelligent communications. After that, several potential techniques and research topics in deep learning-driven wireless communications are pointed out. Finally, the paper is concluded.

Emerging deep learning Technologies in Wireless Communications

A list of emerging technology initiatives of incorporating AI schemes for communication research is provided by IEEE Communications Society. Footnote 1 This section selects and introduces the latest research progress of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation, channel estimation and compression sensing, encoding and decoding, and security and privacy.

End-to-end communications

The guiding principle in communication system design is to decompose signal processing into chains with multiple independent blocks. Each independent block performs a well-defined and isolated function, such as source coding/decoding, channel coding/decoding, modulation, channel estimation and equalization [ 15 ]. This kind of approach yields today’s efficient, versatile, and controllable wireless communication systems. However, it is unclear whether the optimization of individual processing blocks can achieve optimal end-to-end performance, while deep learning can realize theoretically global optimal performance. Thus, deep learning has produced far-reaching significance for wireless communication systems and has shown promising performance improvements.

As shown in Fig.  1 , an autoencoder consists of an encoder and a decoder, where the input data is first processed by the encoder at the transmitter, and then it is decoded at the receiver in order to get the output. The transmitter encodes the input s as a one-hot vector, and a conditional probability density function p ( y|x ) is applied to indicate the wireless channel. After receiving the message, the receiver selects the one with the maximum probability over all possible messages as the output \( \hat{s} \) [ 10 ]. Autoencoder is mainly constructed by neural networks, i.e., an encoding network and a decoding network, the wireless communication system is divided into multiple physical layers to facilitate the propagation of information via the neural network thereon.

Autoencoder-based communication systems

In addition, the idea of end-to-end learning in communication systems has also attracted widespread attention in the wireless communications community [ 16 ]. Several emerging trends for deep learning in communication physical layer were elaborated in [ 10 ]. By treating the wireless communication system as an autoencoder, redefining it as the transmitter and receiver, a local optimum of the end-to-end refactoring process can be achieved. Moreover, different conditions were set in the physical layer to simulate different transmission environments in reality.

The design paradigms of conventional wireless communication systems have to consider the influence of various uncertain factors in hardware implementation, and compensate for delay and phase, which is not efficient and scalable. In contrast, model-free training of end-to-end communication systems based on autoencoder was built by hardware implementations on software-defined radios (SDRs) [ 17 , 18 ], which was simpler, faster, and more efficient. Furthermore, the first entire neural network-based communication system using SDRs was implemented in [ 19 ], where an entire communication system was solely composed of neural networks for training and running. Since such a system fully considered time-varying in the actual channel, its performance was comparable to that of existing wireless communication systems.

A conditional generative adversarial network (CGAN) was applied in [ 20 ] to construct an end-to-end wireless communication system with unknown channel conditions. The encoded signal for transmitting was treated as condition information, and the transmitter and receiver of the wireless communication system were each replaced by a DNN. CGAN acted as a bridge between the transmitter and the receiver, allowing backpropagation to proceed smoothly, thereby jointly training and optimizing both the transmitter and receiver DNNs. This approach makes a significant breakthrough in the modeling mode of conventional wireless communications and opens up a new way for the design of future wireless communication systems.

Signal detection

Deep learning-based signal detection is getting more and more popular. Unlike the conventional model-based detection algorithms that rely on the estimation of the instantaneous channel state information (CSI) for detection, the deep learning-based detection method does not require to know the underlying channel model or the knowledge of the CSI when the channel model is known [ 21 ]. A sliding bidirectional recurrent neural network (SBRNN) was proposed in [ 22 ] for signal detection, where the trained detector was robust to changing channel conditions, eliminating the requirement for instantaneous CSI estimation.

Unlike traditional orthogonal frequency-division multiplexing (OFDM) receivers that first estimate the CSI explicitly, and then the estimated CSI is used to detect or restore the transmitted symbols, the deep learning-based method in [ 23 ] estimated the CSI implicitly and then recovered the transmitted signals directly. The estimated CSI was to solve the problem that a large amount of training data and high training cost were required due to a large increase in the number of parameters caused by DNNs.

Some recent works have suggested the use of DNNs in the context of MIMO detection and have developed model-driven deep learning networks for MIMO detection. For example, a network specifically designed for MIMO communication [ 24 ] can cope with time-varying channel in only one training phase. Instead of addressing a single fixed channel, a network obtained by unfolding the iterations of a projected gradient descent algorithm can handle multiple time-invariant and time-varying channels simultaneously in a single training phase [ 25 ]. Deep learning-based networks as demonstrated in [ 26 ] can reach near-optimal detection performance, guaranteed accuracy and robustness with low and flexible computational complexity.

Channel estimation and compression sensing

Channel estimation and compression sensing are key technologies for the real-time implementation of wireless communication systems. Channel estimation is the process of estimating the parameters of a certain channel model from the received data, while compression sensing is a technique to acquire and reconstruct sparse or compressible signals. Deep learning-based channel estimation and compression sensing methods have been suggested in several recent works [ 27 , 28 , 29 , 30 ].

To tackle the challenge of channel estimation when the receiver is equipped with a limited number of radio frequency (RF) chains in massive MIMO systems, a learned denoising-based approximate message passing (LDAMP) network was exploited in [ 27 ], where the channel structure can be learned and estimated from a large amount of training data. Experiment results demonstrated that the LDAMP network significantly outperforms state-of-the-art compressed sensing-based algorithms.

Motivated by the covariance matrix structure, a deep learning-based channel estimator was proposed in [ 28 ], where the estimated channel vector was a conditional Gaussian random variable, and the covariance matrix was random. Assisted by CNN and the minimum mean squared error (MMSE) estimator, the proposed channel estimator can ensure the state-of-the-art accuracy of channel estimation at a very lower computational complexity.

The basic architecture of deep learning-based CSI feedback is as shown in Fig.  2 . Recently, more and more researchers have focused on the benefits of CSI feedback that the transmitter can utilize it to precode the signals before the transmission, thus we can gain the improvement of MIMO systems. The precoding technique can help to realize the high quality of restoring signals and are widely adopted in wireless communication systems. By exploiting CSI, the MIMO system can substantially reduce multi-user (MU) interference and provide a multifold increase in cell throughput. In the network of frequency division duplex (FDD) or time division duplex (TDD), the receiver UE can estimate the downlink CSI and transmit it back to the BS once they obtain it and help BS to perform precoding for the next signal. BS can also obtain the uplink CSI to help rectify the transmission at UE. The procedure of CSI feedback transmitting has drawn much attention, since high quality reconstructed CSI received by BS guarantees a good precoding, improving the stability and efficiency of the MIMO system.

Deep learning-based CSI feedback

Inspired by traditional compressed sensing technologies, a new CNN-based CSI sensing and recovery mechanism called CsiNet was proposed in [ 29 ], which effectively used the feedback information of training samples to sense and recover CSI, and achieved the potential benefits of a massive MIMO. The encoder of CsiNet converted the original CSI matrix into a codebook using CNN, and then the decoder restored the received codebook to the original CSI signal using the fully-connected network and refine networks.

To further improve the correctness of CSI feedback, a real-time long short-term memory (LSTM) based CSI feedback architecture named CsiNet-LSTM was proposed in [ 31 ], where CNN and RNN are applied to extract the spatial and temporal correlation features of CSI, respectively. Using time-varying MIMO channel time correlation and structural features, CsiNet-LSTM can achieve a tradeoff between compression ratio, CSI reconstruction quality, and complexity. Compared to CsiNet, the CsiNet-LSTM network can trade time efficiency for CSI reconstruction quality. Further, the deep autoencoder-based CSI feedback in the frequency division duplex (FDD) massive MIMO system was modelled in [ 30 ], which involved feedback transmission errors and delays.

As shown in Fig.  3 , a novel effective CSI sensing and recovery mechanism in the FDD MIMO system was proposed in our previous work [ 32 ], referred to as ConvlstmCsiNet, which takes advantage of the memory characteristic of RNN in modules of feature extraction, compression and decompression, respectively. Moreover, we adopt depthwise separable convolutions in feature recovery to reduce the size of the model and interact information between channels. The feature extraction module is also elaborately devised by studying decoupled spatio-temporal feature representations in different structures.

The architecture of ConvlstmCsiNet with P3D block [ 32 ]

Encoding and decoding

In digital communications, source coding and channel coding are typically required in data transmission. Deep learning methods have been suggested in some recent works [ 33 , 34 , 35 , 36 , 37 , 38 ] that can be used to improve standard source decoding and solve the problem of high computational complexity in channel decoding.

A DNN-based channel decoding method applied in [ 33 ] can directly realize the conversion from receiving codewords to information bits when considering the decoding part as a black box. Although this method shows advantages in performance improvement, learning is constrained with exponential complexity as the length of codewords increases. Therefore, it is neither fit for random codes, nor for codewords with long code lengths.

The issue of joint source encoding and channel encoding of structured data over a noisy channel was addressed in [ 38 ], a lower word error rate (WER) was achieved by developing deep learning-based encoders and decoders. This approach was optimal in minimizing end-to-end distortion where both the source and channel codes have arbitrarily large block lengths, however, it is limited in using a fixed length of information bits to encode sentences of different lengths.

Belief propagation (BP) algorithm can be combined with deep learning networks for channel decoding. Novel deep learning methods were proposed in [ 36 , 37 ] to improve the performance of the BP algorithm. It demonstrated that the neural BP decoder can offer a tradeoff between error-correction performance and implementation complexity, but can only learn a single codeword instead of an exponential number of codewords. Neural network decoding was only feasible for very short block lengths, since the training complexity of deep learning-based channel decoders scaled exponentially with the number of information bits and. A deep learning polarization code decoding network with partitioned sub-blocks was proposed in [ 34 ] to improve its decoding performance for high-density parity check (HDPC) codes. By dividing the original codec into smaller sub-blocks, each of which can be independently encoded/decoded, it provided a promising solution to the dimensional problem. Furthermore, Liang et al. [ 35 ] proposed an iterative channel decoding algorithm BP-CNN, which combined CNN with a standard BP decoder to estimate information bits in a noisy environment.

Security and privacy

Due to the shared and broadcast nature of wireless medium, wireless communication systems are extremely vulnerable to attacks, counterfeiting and eavesdropping, and the security and privacy of wireless communications have received much attention [ 39 , 40 ]. Moreover, wireless communication systems are becoming increasingly complex, and there is a close relationship between various modules of the system. Once a module is attacked, it will affect the operation of the entire wireless communication system.

Running AI functions on nearby edge servers or remote cloud servers is very vulnerable to security and AI data privacy issues. Thus, offloading AI learning models and collected data to external cloud servers for training and further processing may result in data loss due to the user's reluctancy of providing sensitive data such as location information. Many research efforts have focused on bridging DL and wireless security, including adversarial DL techniques, privacy Issues of DL solutions and DL hardening solutions [ 41 , 42 ], to meet critical privacy and security requirements in wireless communications.

Conventional wireless communication systems generally suffer from jamming attacks, while autoencoder-based end-to-end communication systems are extremely susceptible to physical adversarial attacks. Small disturbances can be easily designed and generated by attackers. New algorithms for making effective white-box and black-box attacks on a classifier (or transmitter) were designed in [ 43 , 44 ]. They demonstrated that physical adversarial attacks were more destructive in reducing the transmitter’s throughput and success ratio when compared to jamming attacks. In addition, how to keep security and enhance the robustness of intelligent communication systems is still under discussion. Defense strategies in future communication systems are still immature and inefficient. Therefore, further research on the defense mechanisms of adversarial attacks and the security and robustness of deep learning-based wireless systems is very necessary.

One possible defense mechanism is to train the autoencoder to have an antagonistic perturbation, which is a technique that enhances robustness, known as the adversarial training [ 45 ]. Adversarial deep learning is applied in [ 46 ] to launch an exploratory attack on cognitive radio transmissions. In a canonical wireless communication scenario with one transmitter, one receiver, one attacker, and some background traffic, even the transmitter’s algorithm is unknown to the attacker, it can still sense a channel, detect transmission feedback, apply a deep learning algorithm to build a reliable classifier, and effectively jam such transmissions. A defense strategy against an intelligent jamming attack on wireless communications was designed in [ 47 ] to successfully fool the attacker into making wrong predicts. To avoid the inaccurate learned model due to interference of the adversary, one possible way is to use DNNs in conjunction with GANs for learning in adversarial radio frequency (RF) environments, which are capable of distinguishing between adversarial and trusted signals and sources [ 48 ].

Open challenges

This section discusses several open challenges of deep learning-driven wireless communications from the aspects of baseline and dataset, model compression and acceleration, CSI feedback and reconstruction, complex neural networks, training at different SNRs and fast learning.

Baseline and dataset

The rapid development of computer vision, speech recognition, and natural language processing have benefited most from the existence of many well-known and effective datasets in computer science, such as ImageNet [ 49 ] and MNIST [ 50 ]. For fairness, performance comparisons between different approaches should be performed under the same experimental environment by using common datasets. In order to compare the performance of newly proposed deep learning models and algorithms, it is critical to have some well-developed algorithms serving as benchmarks. Experiment results based on these benchmarks are usually called baselines, which are very important to show the development of a research field [ 51 ]. The quality and quantity of open datasets will have a huge impact on the performance of deep learning-based communication systems.

Wireless communication systems involve inherently artificial signals that can be synthesized and generated accurately, the local bispectrum, envelope, instantaneous frequency, and symbol rate of the signal can be used as input features. Therefore, in some cases, we should pay more attention to the standardization of data generation rules rather than the data itself.

In the field of intelligent wireless communications, however, there are few existing and public datasets that can be directly applied for training. It is necessary to either create generic and reliable datasets for different communication problems or develop new simulation software to generate datasets in various communication scenarios. On the basis of such dataset or data generation software, widely used datasets similar to ImageNet and MNIST can be created. Then, we can treat them as baselines or benchmarks for further comparison and research.

Model compression and acceleration

Deep neural networks (DNN) have achieved significant success in computer vision and speech recognition, in the meanwhile, their depth and width are still boosting, which lead to a sharp increase in the computational complexity of networks. At present, the number of parameters in DNN models is very huge (parameters are generally tens of millions to hundreds of millions) and thus the amount of calculation is extremely large. Current deep learning models either rely on mobile terminals or edge-cloud server to run AI functions and are under tremendous pressure in terms of high data storage and processing demands [ 41 ]. Offloading complex compute tasks from mobile terminals to a central cloud with AI functions can alleviate the limitation of computation capacity, but also results in high latency for AI processing due to long-distance transmissions. Therefore, it is not appropriate to offload AI learning model to the central cloud server, especially for data-intensive and delay-sensitive tasks.

Some deep learning algorithms deployed on mobile terminals can only rely on cloud graphic processing units (GPUs) to accelerate computing, however, the wireless bandwidth, the communication delay, and the security of cloud computing will incur enormous obstacles. The large memory and high computational consumption required by the DNN greatly restricts the use of deep learning on mobile terminals with limited resources. Deep learning-based communication systems are also difficult to deploy on small mobile devices such as smartphones, smartwatches and tablets.

Due to the huge redundancy of the parameters in DNN models, these models can be compressed and accelerated to build a lightweight network, which is an inevitable trend in the development of related technologies in the future. Methods like low-rank factorization, parameter pruning and sharing, quantization, and knowledge distillation can be applied in DNN models. Specifically, on the one hand, it is possible to consider quantifying the parameters of DNN models to further compress the network model; on the other hand, channel pruning and structured sparse constraints can be applied to eliminate part of the redundant structure and accelerate the calculation speed [ 52 ].

Lightweight AI engines at the mobile terminals are required to perform real-time mobile computing and decision making without the reliance of edge-cloud servers, where the centralized model is stored in the cloud server while all training data is stored on the mobile terminals. In addition, learning parameter settings or updates are implemented by local mobile devices. In some cases, if the floating-point calculation or storage capacity of the network model is greatly reduced, but the performance of the existing DNNs remains essentially unchanged, such a network model can run efficiently on resource-constrained mobile devices.

CSI feedback and reconstruction

The massive multiple-input multiple-output (MIMO) system is usually operated in OFDM over a large number of subcarriers, leading to a problem of channel state information (CSI) feedback overload. Moreover, in order to substantially provide a multifold increase in cell throughput, each base station is equipped with thousands of antennas in a centralized or distributed manner [ 29 ]. Therefore, it is crucial to utilize the available CSI at the transmitter for precoding to improve the performance of FDD networks [ 32 ]. However, compressing a large amount of CSI feedback overload in massive MIMO systems is very challenging. Traditional estimation approaches like compressive sensing (CS) can only achieve poor performance on CSI compression in real MIMO system due to the harsh preconditions.

Although DL-based CSI methods outperform much than the CS ones, the price of training cost remains high, which requires large quantities of channel estimates. Once the wireless environment changes significantly, a trained model still has to be retrained [ 53 ]. In addition, a more able and efficient structure of DNN is needed. The design of CSI feedback link and precoding mode still remains an open issue that different MIMO systems should adopt their own appropriate designed CSI feedback link and precoding manner. Furthermore, DL-based CSI feedback models are still immature when adopted in real massive MIMO systems and suffer constraints of realistic factors, e.g., time-varying channel with fading, SRS measurement period, channel capacity limitation, hardware or device configuration, channel estimation and signal interference in MU systems. These challenges may hinder the general applications temporarily and will be addressed by future DL-based models with a more exquisite and advanced architecture.

Complex neural networks

Due to the widely used baseband representations in wireless communication systems, data is generally processed in complex numbers, and most of the associated signal processing algorithms rely on phase rotation, complex conjugate, absolute values, and so on [ 10 ].

Therefore, neural networks have to run on complex values rather than real numbers. However, current deep learning libraries usually do not support complex processing. While complex neural networks may be easier to train and consume less memory, they do not provide any significant advantages in terms of performance. At present, we can only think of a complex number as a real number and an imaginary number. Complex neural networks that are suitable for wireless communication models should be developed.

Training at different SNRs

Up to now, it is still not clear which signal-to-noise (SNR) ratio the deep learning model should be trained on. The ideal deep learning model should be applied to any SNR regardless of the SNR used for training or the range of SNR it is in. In fact, however, this is not the case. The results of training deep learning models under certain SNR conditions are often not suitable for other SNR ranges [ 10 ].

For example, training at lower SNRs does not reveal important structural features of wireless communication systems at higher SNRs, and similarly, training at higher SNRs can not reveal important structural features of wireless communication systems at lower SNRs. Training the deep learning model across different SNRs can also seriously affect the training time. In addition, how to construct an appropriate loss function, how to adjust parameters and data representation for wireless communication systems are still big problems that must be solved.

Fast learning

For end-to-end training of wireless communication systems including encoders, channels, and decoders, a specific channel model is usually required. The trained model needs to be applied to its corresponding channel model, otherwise, mismatch problems will occur, which will cause severe degradation of system performance.

In real-world scenarios, however, due to many environmental factors, the channel environment often changes at any time and place, e.g., the change of the movement speed and direction of user terminals, the change of the propagation medium, the change of the refractive scattering environment. Once the channel environment changes, a large amount of training data is needed to retrain, which means that for different channel environments at each moment, such repeated training tasks need to be performed, which consumes resources and weakens the performance of the system.

Retraining is required when the system configuration changes because the system model does not have a good generalization ability. Adaptation is done on a per-task basis and is specific to the channel model [ 54 ]. Some changes in the channel environment may lead to a sharp decline in system performance. Therefore, we need to seek systems with stronger generalization ability, in order to adapt to the changing channel environment.

Potential opportunities

This section mainly describes the profound potential opportunities and the promising research directions in wireless communications assisted by the rapid development of deep learning.

Deep learning-driven CSI feedback in massive MIMO system

Recent researches indicate that applying deep learning (DL) in MIMO systems to address the nonlinear problems or challenges can indeed boost the quality of CSI feedback compression. Different from the traditional CS-based approaches, DL-based CSI methods adopt several neural network (NN) layers as an encoder replacing the CS model to compress CSI as well as a decoder to recover the original CSI, which can speed up the transmitting runtime nearly 100 times of CS ones.

The structure of autoencoder-based MIMO systems is depicted in Fig.  4 , which only considers the downlink CSI feedback process, assuming that the feedback channel is perfect enough to transmit CSI with no impairments. In fact, a large part of the overload CSI serves redundant and the CSI matrix falls to sparse in the delay domain. In order to remove the information redundancy, CNNs are applied here, which has the ability to eliminate the threshold of domain expertise since CNNs use hierarchical feature extraction, which can effectively extract information and obtain increasingly abstract correlations from the data while minimizing data preprocessing workload.

The structure of autoencoder-based MIMO systems with downlink CSI feedback

We can consider both the issues of feedback delay and feedback errors. Assume that one signal is transmitted into n time slots due to the restriction of downlink bandwidth resource, thus demanding a n -length time series of CSI feedback estimation within a signal transmitting period and the SRS measurement period. The time-varying channel is also under the condition of known overdue CSI or partial CSI characteristics, such as Doppler or beam-delay information. Furthermore, the feedback errors from MU interference brought by multiple UE at middle or high moving speed are also taken into account. When transmitting the compressed CSI feedback, the imperfections, e.g. additive white Gaussian noise (AWGN), in uplink CSI feedback channel would also bring feedback errors. The model is trained to minimize the feedback errors via the minimum mean square error (MMSE) detector.

The architecture of DL-based autoencoder in CSI feedback compression is also advanced via taking the advantages of RNN’s memory characteristic to deal with the feature extraction in time-varying channel, which can have an active effect on time correlation exploring and better performance on CSI recovery [ 30 ]. Similarly, a DL-based autoencoder of CSI estimation method can be applied in this MIMO system, which is exposed to more practical restrictions.

In the future, we can use DL methods of CSI feedback with time-varying channel in massive MU-MIMO system to improve the compression efficiency and speed up the transmitting process, as well as develop novel theoretical contributions and practical research related to the new technologies, analysis and applications with the help of CNN and RNN.

GAN-based Mobile data augmentation

Mobile data typically comes from a variety of sources with various formats and exhibits complex correlations and heterogeneity. According to the mobile data, conventional machine learning tools require cumbersome feature engineering to make accurate inferences and decisions. Deep learning has eliminated the threshold of domain expertise because it uses hierarchical feature extraction, which can effectively extract information and obtain increasingly abstract correlations from the data while minimizing data pre-processing workload [ 55 ]. However, inefficiency in training time is an enormous challenge when applying learning algorithms in wireless systems. Traditional supervised learning methods, which learn a function that maps the input data to some desired output class label, is only effective when sufficient labeled data is available. On the contrary, generative models, e.g., GAN and variational autoencoder (VAE), can learn the joint probability of the input data and labels simultaneously via Bayes rule [ 56 ]. Therefore, GANs and VAEs are well suitable for learning in wireless environments since most current mobile systems generate unlabeled or semi-labeled data.

GANs can be used to enhance the configuration of mobile and wireless networks and help address the growth of data volumes and algorithm-driven applications to satisfy the large data needs of DL algorithms. GAN is a method that allows exploiting unlabeled data to learn useful patterns in an unsupervised manner. GANs can be further applied in B5G mobile and wireless networks, especially in dealing with heterogeneous data generated by mobile environments.

As shown in Fig.  5 , the GAN model consists of two neural networks that compete against each other. The generator network tries to generate samples that resemble the real data such that the discriminator cannot tell whether it is real or fake. After training the GAN, the output of the generator is fed to a classifier network during the inference phase. We can use GAN to generate real data according to previously collected real-world data. Furthermore, it can be used for path planning, trajectories analysis and mobility analysis.

GAN-based mobile data generation

Monitoring large-scale mobile traffic is, however, a complex and costly process that relies on dedicated probes, which have limited precision or coverage and gather tens of gigabytes of logs daily [ 57 ]. Heterogeneous network traffic control is an enormous obstacle due to the highly dynamic nature of large-scale heterogeneous networks. As for a deep learning system, it has difficulty in characterizing the appropriate input and output patterns [ 58 ].

GANs can be applied in resource management and parameter optimization to adapt to the changes in the wireless environment. To make this happen, intelligent control of network traffic can be applied to infer fine-grained mobile traffic patterns, from aggregate measurements collected by network probes. New loss functions are required to stabilize the adversarial model training process, and prevent model collapse or non-convergence problems. Further, data processing and augmentation procedure are required to handle the insufficiency of training data and prevent the neural network model from over-fitted.

Deep learning-driven end-to-end communication

The purpose of autoencoder is to make the input and the output as similar as possible, which is achieved by performing backpropagation of the error and continuing optimization after each output. Similarly, a simple wireless communication system consists of a transmitter (encoder), a receiver (decoder) through a channel, and an abundant of physical layer transmission technologies can be adopted in the wireless communication process. A communication system over an additive white gaussian noise (AWGN) or Rayleigh fading channel can be represented as a particular type of autoencoder. The purpose of wireless communication is to make the output signal and the input signal as similar as possible. However, how to adapt an end-to-end communications system trained on a statistical model to a real-world implementation remains an open question.

As shown in Fig.  6 , we can extend the above single channel model to two or more channels, where multiple transmitter and multiple receivers are competing for the channel capacity. As soon as some of the transmitters and receivers are non-cooperative, adversarial training strategies such as GANs could be adopted. We can perform joint optimization for common or individual performance metrics such as block error rate (BLER). However, how to train two mutually coupled autoencoders is still a challenge. One suggestion is to assign dynamic weights to different autoencoders and minimize the weighted sum of the two loss functions.

Autoencoder-based MIMO System

The diagram of the energy-based generative adversarial network (EBGAN) [ 59 ] in wireless communications is depicted in Fig.  7 . We use an encoder instead of a transmitter, and a decoder instead of a receiver for intelligent communications. The generative network is applied to generate the canonicalized signal, and then fed into the discriminative network for further classification. Inverse filtering can be applied to simplify the task of the learned discriminative network. Similarly, the purpose of EBGAN-based end-to-end communication is to make the output signal and the input signal as close as possible.

EBGAN with an autoencoder discriminator in wireless communications

The discriminator D is structured as an autoencoder:

where Enc (·) and Dec (·) denote the encoder function and decoder function, respectively.

Given a positive margin m, a data sample x and a generated sample G ( z ), the discriminator loss LD and the generator loss LG are formally defined by:

where {·} + is the hinge loss function, the generator is trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to generated samples [ 59 ].

Most mathematical models in wireless communication systems are static, linear, and Gaussian-compliant optimization models. However, a realistic communication system has many imperfect and non-linear problems, e.g., nonlinear power amplifiers, which can only be captured by these models. The EBGAN-based wireless communication system no longer requires a mathematical linear processing model that can be optimized for specific hardware configurations or spatially correlated channels. With the help of EBGAN, we can learn about the implementation details of the transmitter and receiver and even the information coding without any prior knowledge.

Meta-learning to wireless communication

In real-world scenarios, it is not worthwhile to perform multi-tasks training from scratch just because of different channel models, because these tasks are closely related, they share the same encoder and decoder network structure, and their parameter changes are only affected by the channel model. Training from scratch is under the assumption that such tasks are completely independent and cannot make full use of the connections, resulting in many repetitive and redundant training steps, however, it is not true.

Meta-learning, or learning to learn [ 60 ], that is, to make the model a learner. It learns a priori knowledge in multi-tasking and then quickly applies it to the learning of new tasks, so that fast learning and few-shot learning can be realized. Meta-learning provides a way to perform multi-task learning and optimizes the system parameters toward a common gradient descent direction during training, thereby achieving the optimal generalization ability and reduced training data and/or time complexity. In the meantime, when a new task arrives, the system can train on a few rounds of iterative (or even one round of iterative) with very little training data, so that the parameters can be dynamically fine-tuned on the basis of the original learning model to adapt to the new channel model, where the dynamic parameter tuning is possible. Thus, meta-learning can be implemented for end-to-end learning of encoder and decoder with unknown or changing wireless channels, and it outperforms conventional training and joint training in wireless communication systems [ 54 ].

A specific example of meta-training methods known as model agnostic meta-learning (MAML) [ 61 ]. Its core idea is to find a common initialization point that allows for a quick adaptation towards the optimal performance on the new task. MAML updates parameters through one or more stochastic gradient descent (SGD) steps, which are calculated using only a small amount of data from the new task. Therefore, instead of training a common system model for all channel models, we can apply MAML to find a common initialization vector so that it supports fast training on any channel [ 54 ].

Several recent efforts have focused on intelligent communications to harvest remarkable potential benefits. We have mainly discussed the potential applicability of deep learning in the field of wireless communications for edge-cloud computing, such as model-free training method for end-to-end wireless communications, and further demonstrated their superior performance over conventional wireless communications. Implementation of many emerging deep learning technologies are still in the preliminary stage, and profound potential solutions to solving wireless communication problems have to be further studied. This survey attempts to summarize the research progress in deep learning-driven wireless communications and point out existing bottlenecks, future opportunities and trends.

In the research of B5G wireless networks and communication systems, the low efficiency of training time is a bottleneck when applying learning algorithms in wireless systems. Although deep learning is not mature in wireless communications, it is regarded as a powerful tool and hot research topic in many potential application areas, e.g., channel estimation, wireless data analysis, mobility analysis, complicated decision-making, network management, and resource optimization. It is worthwhile to investigate the use of deep learning techniques in wireless communication systems to speed up the training process and develop novel theoretical contributions and practical research related to the new technologies, analysis and applications for edge-cloud computing.

Availability of data and materials

Machine Learning For Communications Emerging Technologies Initiative https://mlc.committees.comsoc . org/research-library.

Abbreviations

Artificial intelligence

Augmented reality

Additive white gaussian noise

Block error rate

Belief propagation

Base station

Beyond the fifth-generation

Conditional generative adversarial network

Convolutional neural network

Channel state information

Deep neural network

Energy-based generative adversarial network

Frequency division duplex

Generative adversarial network

Graphics processing unit

High-density parity check

Learned denoising-based approximate message passing

Long short-term memory

Agnostic meta-learning

Minimum mean squared error

Multiple-input multiple-output

Next-generation network

Orthogonal frequency-division multiplexing

Radio frequency

Recurrent neural network

Software-defined radio

Sliding bidirectional recurrent neural network

Stochastic gradient descent

Time division duplex

• Internet of things

User Equipment

Virtual reality

Word error rate

Fifth-Generation

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Acknowledgements

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

Authors’ informations

Huaming Wu received the B.E. and M.S. degrees from Harbin Institute of Technology, China in 2009 and 2011, respectively, both in electrical engineering. He received the Ph.D. degree with the highest honor in computer science at Free University of Berlin, Germany in 2015. He is currently an associate professor in the Center for Applied Mathematics, Tianjin University. His research interests include mobile cloud computing, edge computing, fog computing, internet of things (IoTs), and deep learning.

Xiangyi Li received the B.S. in Applied Mathematics from Tianjin University, China. She is currently a M.S. student majoring in applied mathematics at Tianjin University, China. Her research interests include deep learning, wireless communications and generative models.

Yingjun Deng received the B.S. in Applied Mathematics (2009) and M.S. in Computational Mathematics (2011) from Harbin Institute of Technology, China. He got his Ph.D. in Systems Optimization and Dependability from Troyes University of Technology, France in 2015. He worked as a postdoctoral fellow, respectively at University of Waterloo in Canada (2015–2016), and Eindhoven University of Technology in Netherlands (2018–2019). He became a lecturer since 2016 in the Center for Applied Mathematics, Tianjin University, China. His research interests include applied statistics, deep learning, prognostic and health management, and predictive maintenance.

This work is partially supported by the National Key R & D Program of China (2018YFC0809800), the National Natural Science Foundation of China (61801325), the Huawei Innovation Research Program (HO2018085138), the Natural Science Foundation of Tianjin City (18JCQNJC00600), and the Major Science and Technology Project of Tianjin (18ZXRHSY00160).

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Wu, H., Li, X. & Deng, Y. Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges. J Cloud Comp 9 , 21 (2020). https://doi.org/10.1186/s13677-020-00168-9

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The fifth generation (5G) wireless communications are expected to satisfy the diverse service requirements in various aspects of our daily life, from residence, work, leisure, to transportation. Due to the extreme range of 5G requirements for user experience, efficiency, performance and complex network environments, the design and optimization of 5G networks becomes very challenging. The future 5G network will require robust intelligent algorithms to adapt network protocols and resource management for different services in different scenarios. Artificial intelligence (AI), which is defined as any process or device that perceives its environment and take actions that maximize the chances of success for some predefined goal, is a feasible solution for the emerging complex communication system design. The recent advances in deep learning, convolutional neural networks and reinforcement learning hold significant promise for solving very complex problems considered intractable until now. It is now appropriate to apply AI technology to 5G wireless communications to tackle optimized physical layer design, complicated decision making, network management and resource optimization tasks in such networks. Moreover, the emerging big data technology has brought us an excellent opportunity to study the essential characteristics of wireless networks, and to help us to obtain more clear and in-depth knowledge of the behavior of 5G wireless networks. In the study of 5G wireless technologies and communication systems, AI will be a powerful tool and hot research topic with many potential application areas, e.g., wireless signal processing, channel modeling, and resource management.

This IEEE Communications Magazine Feature Topic (FT) aims to provide a comprehensive overview of the state-of-the-art development in technology, regulation and theory for “applications of artificial intelligence in wireless communications," and to present a holistic view of research challenges and opportunities in the coming area of 5G wireless communications. Suggested topics include but are not limited to the following:

• Novel design of deep-learning and convolutional neural network approaches for wireless system applications and services.
• Novel design of machine-learning and pattern recognition algorithms for wireless communication technologies.
• Applications of AI for optimizing wireless communication systems, including channel models, channel state estimation, beamforming, code book design and signal processing.
• Applications of AI for 5G wireless transmission technologies, including coordinated multiple points transmission/reception, large scale antenna array, and multi-hop relay.
• Applications of AI for 5G mobile management, including user association, handoff strategy, and backhaul technology.
• Applications of AI for 5G resource management, including spectrum resources, energy sources, cloud resources, computing resources, and communication infrastructure.
• The analysis and prediction of 5G network behaviour via AI technologies, including the multi-media traffic load, network overhead, and network collision.
• Evaluating the scope for and potential limitations of AI solutions in wireless communications.

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Manuscript Submission Guidelines .

All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select the "March 2019/Applications of Artificial Intelligence in Wireless Communications" topic from the drop-down menu of Topic/Series titles.

Important Dates

Manuscript Submission Deadline: August 1, 2018 Decision Notification: December 1, 2018 Final Manuscript Due: January 1, 2018 Publication Date: March 2019

Guest Editors

Xiaohu Ge Huazhong University of Science and Technology, Wuhan, China

John Thompson The University of Edinburgh, Scotland, UK

Yonghui Li University of Sydney, Australia

Xue (Steve) Liu McGill University, Montreal, Canada

Weiyi (Max) Zhang AT&T Labs Research, Middletown, USA

Tao Chen VTT Technical Research Centre of Finland Ltd., Oulu Finland

Editorial: Current and Future Trends in Wireless Communications Protocols and Technologies

• Published: 08 March 2018
• Volume 23 , pages 377–381, ( 2018 )

Cite this article

• Muhammad Alam 1 ,
• Mian Ahmad Jan 2 ,
• Lei Shu 3 ,
• Xiangjian He 4 &
• Yuanfang Chen 5  

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Information and Communications Technology (ICT) has significantly progressed in recent years by relying heavily on wireless communications. Currently, most of the devices and systems are connected via wireless technologies and numerous emerging solutions and applications are proposed based on wireless communications. Further, with the introduction of concepts such as Internet of Things (IoTs), nearly countless number of devices will be connected in a global network through wireless interfaces, using standard protocol solutions. The connectivity of these plethora of devices will be heavily relying on wireless communications and therefore, the challenges such as scalability, reliability, signal coexistence, data rate and energy consumption faced by the existing systems need to be thoroughly analyzed. In addition, the proposal for 5G technologies aims to provide very high data-rates, massive number of devices connectivity, very high reliability and low latencies. Therefore, efforts are underway to consider beyond state of-the-art protocols and mechanisms for wireless communication. Thus, the next generation of wireless communication is expected to meet the demands of various challenging use cases that go far beyond distribution of voice, video and data. Therefore, this special issue aims to present the solutions that address the challenges wireless communication systems are facing.

Therefore, European Alliance for Innovation (EAI) took a step towards the realization of Future Intelligent Vehicular Technologies based on dependable and real-time communication and invite both academic and industrial research community by organizing the 2nd edition of Future 5 V conference in Islamabad, Pakistan. Future 5 V is an annual international conference by EAI (European Alliance for Innovation) and co-sponsored by Springer. Future 5V received more than 150 research articles in field of Vehicular networks/communications covering theory and practices in the after mentioned field of study. The call-for-papers of this SI was an outcome of the 2st EAI International Conference on Future Intelligent Vehicular Technologies and open submission. Following are the details of the accepted papers in this special issue.

The first paper is “The Model Design of Mobile Resource Scheduling in Large Scale Activities” by Cai-Qiu Zhou et al. In this paper, the authors have presented work on multi-resource scheduling model based on Dijkstra and multi-ant colony optimization algorithms. The concept model of mobile resource scheduling is put forward, and the detailed introduction of each dimension is presented and multi-resource scheduling model based on Dijkstra and multi-ant colony optimization algorithms are established respectively. Furthermore, the advantages and disadvantages of the two algorithms are compared through the numerical examples. It has been presented that Dijkstra algorithm is superior to multi-ant colony optimization algorithm in the cost control, but in the running time of the algorithm, multi-ant colony optimization algorithm is better than Dijkstra algorithm. The research has practical significance for the development of scientific and effective population service operation plan and service management plan for large scale exhibition activities.

The next accepted paper is “A Novel On-Line Association Algorithm for Supporting Load Balancing in Multiple-AP Wireless LAN” by Liang Sun et al. It has been presented that wireless LAN has become the most widely deployed technology in mobile devices for providing Internet access. Operators and service providers remarkably increase the density of wireless access points in order to provide their subscribers with better connectivity and user experience. As a result, WLAN users usually find themselves covered by multiple access points and have to decide which one to associate with. The authors have proposed a novel on-line association algorithm to deal with any sequence of STAs during a long-term time such as one day. The performance of the proposed algorithm is evaluated through simulation and experiments. Simulation results show that our algorithm improves the overall WLAN throughput by up to 37%, compared with the conventional RSSI-based approach. The presented algorithm also performs better than SSF (Strongest Signal First) and LAB (Largest Available Bandwidth) in the experiments.

The next paper is “GCC: Group-based CSI Feedback Compression for MU-MIMO Networks” by Jian Fang et al. The Multi-user Multiple Input Multiple Output networks (MU-MIMO) adopts beamforming to enable Access Point (AP) to transmit packets concurrently to multiple users, which brings formidable overhead. The overhead of collecting Channel State Information (CSI) feedback matrix may even overwhelm real-data transmission when the scale of network is large, which incurs unsatisfactory performance and huge waste of resources. Therefore, in this paper, the authors have addressed this problem using GCC which is a Group-based CSI feedback Compression scheme for MU-MIMO networks. It enables users to feedback their CSI in terms of group determined by their location. The GCC limit the quantity of CSI feedback in each transmission round regardless of the size of network by allowing the location-related users to share a CSI matrix. In addition, the authors have used a novel metric to do the tradeoff between throughput and capacity loss of the system. GCC has been tested in different scenarios and compared it with existing works. The evaluation result showed that GCC achieved much higher throughput and is robust to various situations.

Since the The growth and adoption of the Internet of Things (IoT) is increasing day by day and it has become a very hot research topic. We have considered a paper for publication that has presented a Framework for trust management in IoT. The paper is tittled “Clustering-Driven Intelligent Trust Management Methodology for The Internet of Things” by Mohammad Dahman Alshehri et al. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. The authors have proposed a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.

A comprehensive study on designing an energy-aware architectures for Wireless Sensor Networks is presented in the next accepted paper. The paper is “Designing an Energy-Aware Mechanism for Lifetime Improvement of Wireless Sensor Networks: A Comprehensive Study”. The authors have also analysed and proposed a scheme, Extended-Multilayer Cluster Designing Algorithm (E-MCDA) in a large network. Among its components, algorithms for time slot allocation, minimising the CH competition candidates, and cluster member selection to CH play underpinning roles to achieve the target. The authors have done simulations in NS2 to evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, the number of designed clusters, the number of nodes per cluster and unclustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCDA and EADUC.

The next paper is “Rule based (Forward Chaining/Data Driven) Expert System for Node Level Congestion Handling in Opportunistic Network” by Ahthasham Sajid et al. The paper is about Opportunistic networks which are part of the most popular categories of Mobile Ad hoc networks. One of the challenge is the selection of best custodian node that can store messages at its buffer until a destination node is found. The important features of the Delay Tolerant Network (DTN) are a selection of the best forwarding nodes and co-ordination among the nodes to deliver the packets to their destination in an efficient manner with less loss and maximum delivery rate. Therefore, in this paper, the authors have presented a rule based efficient expert system to address and handle storage level congestion issues. The proposed technique has been tested and validated via Opportunistic network environment and compared with MaxProp protocol.

The next paper is “A Comprehensive Analysis of Congestion Control Protocols in Wireless Sensor Networks” by Mian Ahmad Jan et al. In wireless Sensor Networks (WSNs) congestion occurs when the incoming traffic load exceeds the available capacity of the network. The authors have also presented the various factors that lead to congestion in WSNs such as buffer overflow, varying rates of transmission, many-to-one communication paradigm, channel contention and the dynamic nature of a transmission channel. The energy-efficient congestion control protocols need to be designed to detect, notify and control congestion effectively. The authors have present a review of the latest state-of-the-art congestion control protocols. Depending on their inherent nature of control mechanism, these protocols are classified into three categories, i.e., traffic-based, resource-based and hybrid. Traffic-based protocols are further subdivided, based on their hop-by-hop or end-to-end delivery modes. Resource-based control protocols are further analyzed, based on their route establishment approach and efficient bandwidth utilization techniques. In addition, they have discussed the internal operational mechanism of these protocols for congestion alleviation. The authors have concluded that the behaviour of each class of protocols varies with the type of application and a single metric alone cannot precisely detect congestion of the network.

In the last decade, there has been a considerable development in the field of wireless vehicular communications so as to satisfy the requirements of Cooperative Intelligent Transportation Systems (CITS). It is also worth mentioning that currently CITS and intelligent transportation systems are very hot research areas and especially the security of these systems. We have accepted the paper “Implementation and Analysis of IEEE and ETSI Security Standards for Vehicular Communications” by Bruno Fernandes et al. The paper presents the implementation and analysis of the two most used standards for vehicular communications. However, due to the expected popularity of ITS, VANETs could be prone to attacks by malicious sources. To prevent this, security standards, such as IEEE 1609.2 and ETSI ITS’ standards, were developed. In this work, the design and implementation of an API capable of conducting the required cryptographic algorithms and protocols for the transmission of secure messages according to the IEEE 1609.2 and ETSI ITS’ security standards is presented. The implemented security protocols are then integrated into a system emulating a public key infrastructure to evaluate the performance impact on safety-related communications, in particular, the delay associated with the communication’ coding/decoding process.

The next paper is “Performance of Cognitive Radio Sense-and-Wait assisted Hybrid Automatic Repeat reQuest” by Fazlullah Khan et al. In this paper the authors have presented a work on the the cognitive radio (CR) concept which emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. The authors have presented the model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, they have proposed a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). The proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results.

The next accepted paper is “Opportunistic Energy Cooperation Mechanism for Large Internet of Things” by Jinyu Hu et al. In this paper, the authors have focus on energy efficiency maximization and network throughput optimization problems for energy cooperation in Energy Harvesting Cooperative Wireless Sensor Networks (EHC-WSNs). In order to maximize the efficiency of energy charging phase, a Region-based Proactive Energy Cooperation (RPEC) strategy is developed, which is used to charge the life-critical cooperators or receivers in time. Furthermore, by introducing a novel metric that converts optimal forwarder selection from the multi-dimensional problem to one-dimensional problem, an Energy-Neutral based Opportunistic Cooperative Routing (ENOCR) algorithm is proposed to optimize the relay nodes selection and improve the network throughput. The simulations results showed that the proposed mechanism can significantly improve energy efficiency and network lifetime.

The last paper is “User-centric Clustering and Beamforming for Energy Efficiency Optimization in CloudRAN”. In this paper, the authors have considered the problem of how to assign each user to several preferred remote radio heads (RRHs) and design the corresponding beamforming coefficients in a user-centric and energy efficient manner. They have formulated the problem as a joint clustering and beamforming optimization problem, with the objective to maximize the energy efficiency (EE) while satisfying the users’ quality of service (QoS) requirement and respecting the RRHs’ transmit power limits. They have first transform it into an equivalent parametric subtractive problem using the approach in fractional programming, and then it is cast into a tractable convex optimization problem by introducing a lower bound of the objective function. Finally, the structure of the optimal solution is derived and a two-tier iterative scheme is developed to find the clustering pattern and beamforming coefficients that maximize EE. Specially, they have derived a RRH-user association threshold, based on which the RRH clustering pattern and the corresponding beamforming coefficients can be simultaneously determined.

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Instituto de Telecomunicações–Aveiro Campus, Universitário de Santiago, 3810–193, Aveiro, Portugal

Muhammad Alam

Abdul Wali Khan University Mardan, Mardan, Pakistan

Mian Ahmad Jan

Guangdong University of Petrochemical Technology, Maoming, China

University of Technology Sydney, Ultimo, Australia

Xiangjian He

School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China

Yuanfang Chen

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Alam, M., Jan, M.A., Shu, L. et al. Editorial: Current and Future Trends in Wireless Communications Protocols and Technologies. Mobile Netw Appl 23 , 377–381 (2018). https://doi.org/10.1007/s11036-018-1026-y

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Published : 08 March 2018

Issue Date : June 2018

DOI : https://doi.org/10.1007/s11036-018-1026-y

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https://www.nist.gov/programs-projects/future-wireless-communications-systems-and-protocols

Future Wireless Communications Systems and Protocols

5G and beyond communications will include several technical advancements that enable innovative applications such as wireless backhauling, Augmented/Virtual Reality (AR/VR), 8K video streaming and sensing. This project is focused on system-level insights and performance analyses of emerging wireless protocols and standards. Our goal is to use measurement-based models for wireless propagation in the design, the development and the evaluation of next generation wireless communications systems. We work with industry and the research community to improve the accuracy and availability of system-level modeling tools dedicated for such purposes. 

Description

Millimeter wave communication systems and protocols.

5G and beyond wireless communication systems make use of the Millimeter-Wave (mmWave) band.  While this band offers unprecedented throughput thanks to the large bandwidth available, it also suffers from larger propagation loss compared to the sub-6 Ghz band. This characteristic imposes a complete rethinking of the traditional communication paradigm, i.e., omni-directionality. Indeed, to guarantee adequate received signal power, mmWave systems employ directional beams formed by antenna arrays, i.e., beamforming. Beamforming focuses the power (both in transmission and in reception) towards the chosen direction by properly steering the antenna elements of the antenna array. 

The directionality of the communication poses a number of new challenges, including : 

Beamforming requires to select (Beamforming Training) and adapt (Beamforming Tracking) the directional beams to use between two nodes that are willing to communicate. Beamforming Training and Tracking is of paramount importance for mmWave communications as it drives the performance of the communication links. The initial approach, introduced in IEEE 802.11ad and 3GPP release 15, has been to perform an exhaustive search among all the possible beams combinations. However, there are obvious scalability issues with this approach, especially in the context of MIMO systems. Therefore more efficient beamforming Training/Tracking algorithms are needed.   

Most wireless and application protocols have been designed with omnidirectional communications in mind. on the other hand, highly directional communications will impose drastic changes on radio-resource allocation schemes in order to take full-benefits of the directionality of the communication. 

The usage of multiple antennas to compensate for the high propagation loss, enables the PHY to exploit spatial multiplexing techniques to achieve even greater throughput. The design of MIMO precoding and equalization algorithms requires channel estimation techniques tailored to the mmWave propagation characteristics.  

A realistic platform that accurately characterizes the propagation environment is vital to evaluate and develop efficient beamforming, radio-resource allocation and channel estimation algorithms and models, as well as antenna array systems that will address these challenges. Until now, research and development has been conducted separately using tools and platforms that are not integrated and generally not compatible. This presents a major impediment to the development and deployment of future generation millimeter wave systems. Our response is to develop a realistic platform that includes accurate characterization of the channel propagation environment: The Quasi-Deterministic (Q-D) Framework. 

Quasi-Deterministic (Q-D) Framework  

While the development of standard specifications for IEEE 802.11ad/ay is complete, innovative research using IEEE 802.11ad/ay devices is still challenging due to the prohibitive cost of test-bed equipment and the lack of open-source and flexible platforms. The NIST Q-D framework aims to overcome some of these challenges by providing researchers in the mmWave community a set of high-fidelity tools to evaluate and better understand the inter-workings of the IEEE 802.11ad/ay protocols.  

Evaluating performance end-to-end often requires the following: 

An accurate representation of the channel model at 60 GHz. 

A flexible and high-fidelity antenna model.  

PHY model and digital baseband transceiver PHY abstractions. 

A system-level simulator that implements the MAC and PHY specifications of IEEE 802.11ad/ay and that includes realistic channel models, antenna models, and PHY layer abstractions.  

To this end, we developed in collaboration with IMDEA and the University of Padova five different open-source tools as displayed in Figure 1.  

The NIST Q-D Channel Realization Software 1 : This Matlab tool implements the channel between nodes/antennas pairs in the network using ray tracing and Q-D methodology ( link ). The channel is described through the Multi-Path Components (MPCs) properties such as number of MPCs, path loss, delay, angles of arrival and departures. User can define its own 3D environment and nodes mobility/rotations. This software includes a visualizer to display the generated MPCs between each pair of nodes. 

The Codebook Generator 2 : This Matlab tool generates Phased Antenna Arrays properties such as steering vectors and Antenna Weight Vectors based on user-defined Phased Antenna Arrays characteristics (geometry, number of elements, etc.).  

The Integrated Sensing and Communication Physical Layer Model 3 : This tool is a Matlab implementation enabling baseband link-level simulation of millimeter-wave (mm-wave) wireless communication and sensing systems. ISAC-PLM models the Physical Layer (PHY) of IEEE 802.11ay, including a growing set of features, such as Multi-User Multiple-Input Multiple-Output (MU-MIMO) link level simulation, IEEE 802.11ay single carrier (SC)/orthogonal frequency-division multiplexing (OFDM) waveform generation, synchronization, channel estimation, carrier frequency offset (CFO) estimation and correction. It provides an end-to-end simulation platform, including transmitter and receiver, importing the channel realized by the Q-D Channel Realization Software.   The software allows to compute several metrics, such as bit error rate, packet error rate, target range and target velocity estimation accuracy.  It can be used to export lookup tables of packet error rate varying SNR, in AWGN or mm-wave channel, for different modulation and coding schemes.

The ns-3 802.11ad/ay with Q-D channel implementation 4 : The ns-3 system-level simulator has been modified to include IEEE 802.11ad/ay functionalities. Our implementation imports the channel realized by the NIST Q-D Channel Realization Software, the antenna characteristics produced by the Codebook Generator, and includes the PHY layer abstraction by the NIST 802.11ay PHY to obtain high-fidelity system-level evaluation.  

The NIST Q-D Interpreter 3 : IEEE 802.11ad/ay system-level performance highly depends from the beamforming applied at the transmitter/receiver side. To help interpretation of the beamforming training results, we developed a python 3D visualizer that takes as an input the beamforming results obtained in ns-3 and displays the antenna patterns for a transmitter/receiver communication.  

____________________________________________________________________

• Developed by NIST in collaboration with University of Padova SIGNET Group
• Developed by IMDEA WNG Group
• Developed by NIST
• Developed by IMDEA WNG Group in collaboration with NIST

Integrated Communication and Sensing Systems

Sensing and communication systems are competing technologies, sharing the same spectrum and using similar hardware components. The idea of Integrated Sensing And Communication (ISAC) systems has recently gained the attention of research and standardizations communities. Co-designing sensing and communication systems allows to efficiently re-use the spectrum and the hardware resources, for example reusing the communication waveforms and devices to enable sensing applications. For this reason, integrated communication and sensing has been identified as an enabling technology for 5G/6G, and the next-generation Wi-Fi system.

The ubiquitous presence of WiFi devices in our everyday life offers a unique opportunity to enable innovative applications such as presence detection, gesture recognition, or person identification, re-using existing WiFi devices. IEEE has recently (September 2020) started a new task group, TGbf, to extend the current IEEE 802.11ay high throughput functionalities, with cm-level sensing resolution.

IEEE 802.11bf envisions to enable sensing for a wide range of applications such as gesture recognition, number of persons in a room, breathing activity, etc. The requirements for these applications can be vastly different, e.g., detect an object proximity requires less amount of information and accuracy compared to detect a person and identify its pose.

The design of integrated communication and sensing systems poses several challenges including:

• Resource allocation: The sensing accuracy performance will be strongly dependent on the availability and update frequency of the channel measurements. However, as IEEE 802.11bf re-uses the communication link, sending and exchanging sensing information can be solely seen as an overhead from a WiFi performance point-of-view, reducing throughput and increasing latency. In this case, the larger the overhead, the better the sensing accuracy will be at the cost of a lower transmission data rate.
• Cooperation and scheduling:  cooperative sensing (CSENS) is foreseen as a key enabler of high-resolution sensing. CSENS will use multiple sensing devices collaborating to capture additional information about the surrounding environment (e.g., in case of blockage between a sensing device and a target, another sensing device could still be able to sense the target). CSENS will also incur additional overhead as it will not only require more entities in the sensing framework but will also raise new challenges such as synchronization between the multiple entities, establishment of a distributed sensing measurements, fusion of partial sensing information, etc.
• Beamforming training at mm-wave: the optimization of the beamforming training to maximize the communication performance creates very narrow beams. The limited field of view does not allow to have sense the entire environment.

The NIST ISAC Framework  

The Q-D channel model realization software will be used as a baseline to create the NIST ISAC Channel Realization software by introducing the concept of targets, i.e., person or object to sense. While the NIST ISAC Channel Realization software can support different target models, our current study focus on human targets, which can enable several applications such as presence detection and localization.  The human motion is modeled with kinematic models, which approximate the human body as a collection of joints. The NIST 802.11ay PHY is enhanced with dedicated ISAC sensing signal processing to obtain range-doppler information, which allows the detection of moving objects. Finally, the NIST Q-D interpreter is extended to allow the visualization of the interaction between the wireless signal and the human target, while displaying the correspondent range-doppler map.

PUBLICATIONS

Beamforming training.

• M. Kim, T. Ropitault, S. Lee, N. Golmie, H. Assasa, and J. Widmer, "A Link Quality Estimation-based Beamforming Training Protocol for IEEE 802.11 ay MU-MIMO Communications", in IEEE Transactions on Communications, Vol. 69, No. 1, January 2021. 
• M. Kim, T. Ropitault, S. Lee and N. Golmie, "Efficient MU-MIMO Beamforming Protocol for IEEE 802.11ay WLANs", in IEEE Communications Letters, Vol. 22, No. 1, January 2019. 
• Y. Kim, S. Lee and T. Ropitault, "STS Adaptation for Beamforming Training of Asymmetric Links in IEEE 802.11ay-based Dense Networks", in Proceedings of IEEE Vehicular Technology Conference (VTC Spring 2020), June 2020. 
• Y. Kim, S. Lee, and T. Ropitault, "Adaptive Scheduling for Asymmetric Beamforming Training in IEEE 802.11ay-based Environments", in Proceedings of IEEE Wireless Communications and Networking Conference (WCNC 2019), April 2019. 

PHY Layer Evaluation

• J. Zhang, S. Blandino, N. Varshney, J. Wang, C. Gentile and N. Golmie, "Multi-User MIMO Enabled Virtual Reality in IEEE 802.11ay WLAN," 2022 IEEE Wireless Communication and Networking Conference.
• A. Bodi, J. Zhang, J. Wang, and C. Gentile, " Physical-Layer Analysis of IEEE 802.11ay using a Channel Fading Model from Mobile Measurements", in IEEE  International Conference on Communications (ICC 2019), May 2019. 
• N. Varshney, J. Zhang, J. Wang, A. Bodi and N. Golmie, "Link-Level Abstraction of IEEE 802.11ay based on Quasi-Deterministic Channel Model from Measurements," in Proc. of 2020 IEEE Vehicular Technology Conference (VTC2020-Fall), April 2020.  
• S. Blandino, T. Ropitault, A. Sahoo and N. Golmie, "Tools, Models and Dataset for IEEE 802.11ay CSI-based Sensing," 2022 IEEE Wireless Communication and Networking Conference.

T. Ropitault, S. Blandino, N. Varshney and N. Golmie, "Q-D simulation & Modeling framework for sensing", presented at TGbf.

S. Blandino, T. Ropitault, N. Varshney and T. Ropitault, "A preliminary channel model using raytracing to detect human presence", presented at TGbf

Hybrid MAC Performance

• C. Pielli, T. Ropitault, N. Golmie, and M. Zorzi, “An analytical model for CBAP allocations in IEEE 802.11ad,” in IEEE Transactions on Communications, vol. 69, no. 1, 2021.
• J. Chakareski, M. Khan, T. Ropitault, and S. Blandino, “6DOF virtual reality dataset and performance evaluation of millimeter wave vs. free-space-optical indoor communications systems for lifelike mobile VR streaming,” in 2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020.
• T. Azzino, T. Ropitault and M. Zorzi, "Scheduling the Data Transmission Interval in IEEE 802.11ad: A Reinforcement Learning Approach," 2020 International Conference on Computing, Networking and Communications (ICNC), May 2020. 
• C. Pielli, T. Ropitault, and M. Zorzi    "The Potential of mmWaves in Smart Industry: Manufacturing at 60 GHz", in International Conference on Ad-Hoc Networks and Wireless, AdHoc Now 2018, pp. 64-67. 

The Q-D framework

• H. Assasa, N. Grosheva, T. Ropitault, S. Blandino, N. Golmie, and J. Widmer, “Implementation and evaluation of a wlan IEEE 802.11ay model in network simulatorns-3,” in Proceedings of the Workshop on Ns-3, WNS3 ’21, (New York, NY, USA), 2021.
• H. Assasa, T. Ropitault, S. Lee and N. Golmie, "Enhancing the ns-3 IEEE 802.11ad Model Fidelity: Beam Codebooks, Multi-Antenna Beamforming Training, and Quasi-Deterministic mmWave Channel", in Workshop on ns-3 (WNS3 2019), June 2019. 
• H. Assasa, J. Widmer, J. Wang, T. Ropitault, and N. Golmie, "An Implementation Proposal for IEEE 802.11 ay SU/MU-MIMO Communication in ns-3", in Workshop on Next-Generation Wireless with ns-3 (WNG 2019), June 2019 
• H. Assasa, J. Widmer, T. Ropitault, A. Bodi, and N. Golmie, "High Fidelity Simulation of IEEE 802.11 ad in ns-3 Using a Quasi-deterministic Channel Model", in Workshop on Next-Generation Wireless with ns-3 (WNG 2019), June 2019 

Ultra-Dense networks

• M. Kim, T. Ropitault, S. Lee and N. Golmie, "A Throughput Study for Channel Bonding in IEEE 802.11ac Networks," in IEEE Communications Letters, vol. 21, no. 12, pp. 2682-2685, Dec. 2017. 
• T. Ropitault and N. Golmie, "ETP algorithm: Increasing spatial reuse in wireless LANs dense environment using ETX," 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). 
• T. Ropitault, "Evaluation of RTOT algorithm: A first implementation of OBSS_PD-based SR method for IEEE 802.11ax," 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) 

Major Accomplishments

• Participating in IEEE working groups (TGay, TGbf).
• Over 20 peer-reviewed publications on the evaluation and improvement of IEEE 802.11ax/ad/ay.
• Developed (in collaboration with IMDEA) the first open-source ns-3 IEEE 802.11ay implementation. Main features include MIMO Beamforming Training and Quasi-Deterministic propagation model

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Wireless Communication Research Topics

Wireless communication refers to the data transmission via a wireless communication link that is based on the information of users. It represents the data communication that is implemented wirelessly. Through the wireless channels, two or more devices are connected with each other. It incorporates data transmission wireless procedures and signals for data transmission. In particular, electromagnetic signals are broadcasted between two devices through the air and no need for physical connections by wires. For each research area in wireless, there are some research problems incorporated which are necessary to discuss in the wireless communication research topics.

We work in all wireless communication technologies with different forms, and delivery methods for a wide range of communications such as  mobile network communication, infrared communication, satellite communication, and cellular communication . Let’s starts with the wireless communication research areas as follows,   

Wireless Communication Research Areas 

• Cellular Communication in Large Scale Networks
• M2M Communication and MTC
• Vehicular Communication (V2X, V2V, V2I, etc.)
• Ultra-Reliable Low Latency Communications
• D2D Communications
• Biological and 5G Molecular Communications
• Small Cell and HetNets
• Optical Communication
• Next-generation Non-RF Communication   

Research areas in wireless communication are broad. We have aforementioned a few sets of research areas as a reference for you in picking wireless communication research topics . On diverse real-time applications, wireless communication has suffered from more challenges in 6G, 5G, cognitive radios and UWB, etc. As a matter of fact, we give a short summary of the research gaps presented in wireless communication.

What are the important research gaps in wireless communication?

• Coexistence of Multiple RF Bands:  In wireless communication, appropriate bands are not available as a result of non-availability between devices
• Design of Radio Receivers and Transmitters with varied bands:  However, ZIF receivers are complex for the different spectrums
• Interference: Precision is not achieved in real-time applications of wireless communication due to the collision between wireless channels, which is called interference.

Some of the other research issues in wireless communication are discussed in the following,  

Research Issues in Wireless Communication 

• Transmission Range
• Transmission Medium quality
• Sender and Receiver Quality
• Connection Quality
• Proper Supply of Power

The above-mentioned research issues are common for many types of wireless communication. Currently, wireless communication technologies such as 5GB and 6G are increasing in various cellular and autonomous communications . Currently, there are a number of wireless communication research topics are working in 6G and 5G beyond communications. For instance, it is a great communication technology for the Internet of Things and Inter-Vehicle Communications. In the following, we illustrate wireless communication in these two technologies.   

5GB and 6G in Wireless Communication  

                Due to the support of Terahertz Communication, 5GB and 6 G-based wireless cellular communications have grown recently. For resource-constrained devices, large available bandwidth is supplied by the 6G communication . Let’s check out some important features about the 6G environment,   

What are the key features in 6G Wireless Communications?

• Supports high volume of bandwidth and data rates
• Offers reliable communications
• Used in various applications such as VR and AR and autonomous vehicles tracking
• Directly links to provide the high QoS and QoE
• Hence it provides High Rate and High-Reliability Low Latency Communications (HRLLC)
• THz enabled 6G communication systems for uncertainty handling

Recent research and development team from us has initiated 6G technology for the supply of significant features to the real-time application’s design. We at PhD thesis writing in wireless communication have started to work on some research ideas of wireless communication. Some of the ideas are listed for getting research information about the 6G technology. 

6G Wireless Communication Research Topics

• Coexistence of NAMO and OFDMA
• UWB for Multimedia Streaming
• Small Cell Management and Optimization
• Multi-RAT Slicing for Secure HetNets
• Multi-Traffic Classification (QoS and QoE)
• Service-Oriented Interoperability (6G and 5GB)

There are several research ideas are available rather than the above. Therefore, contact our research people for knowing latest communication information and technologies available. Since this will be useful in improving the network performance. Now, look at how to write and what are the important research contributions, wireless research proposal , theory, design, and results are covered in the PhD thesis writing.  

“PhD thesis is the main part of the research journey and it shows the research achievements and empirical results conducted in any field.”  

A complete part of the thesis and its statements are detailed during our PhD thesis writing stage!!!!!  

How to write PhD thesis writing? 

The structure/format of PhD thesis is important to start writing it. Hence, we provide some steps in our thesis writing.

• Thesis writing by its technical contributions and research motivation i.e. Logical Structure has been followed in the PhD thesis.
• Provide the simulation/experiment results, design, analysis, and discussion , in which how and why the research methodology is important in addressing the current research problems in this area and it is used to solve the results validation section by a brief comparison table.
• How do the results fair in comparison with other methods?
• How well did the other methods work?
• What are the limitations of your work?
• What do the results mean (This is provided briefly in the last paragraph, but elaborated on)?
• What experiments were conducted?
• What were the conclusions from the results obtained?
• What is the future scope?
• What are the implications of your research?

We hope that our research ideas and tips for PhD thesis writing are useful for your research career. Our objective is to help for making the original research/study contributions that existing research is not focused on. We will help you to choose novel wireless communication research topics . All the research contributions are highlighted clearly in your thesis writing stage. For further info, contact us for your bright and peaceful research accomplishment.

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Subproject 3: Research Topics on Wireless Communications

Principal investigator: Prof. Bor-Sen Chen

Motivation and Target

Wireless communication is currently an astonishingly prosperous high-technology industry in the world. At present, Taiwan has very large productivity in this industry. However, Taiwan only plays a role of OEM. Actually, the research and development in wireless communication has been quite flourishing in Taiwan, especially playing a leading role in many relevant cutting-edge academic researches. In this subproject, we would like to focus on the following research topics to further promote wireless communication in Taiwan to the world-class level: (1) Cognitive Radio Systems, (2) Cooperative and Secure Wireless Communications, (3) Broadband Wireless Communications, (4) Coding Theory and System Optimization, (5) Communication IC. If these researches can be thoroughly studied and the research results can be advisably integrated in an optimal fashion, we will provide a great platform for training talented creative researchers and capable engineers to significantly promote the wireless communication industry of Taiwan.

The research topics of Subproject 3 have 5 items. They include Wireless Sensing, Cooperative Wireless Communication and It Security, Broadband Wireless Communications, Coding Theory and System Optimization and Low- Power Cognitive Radio SOC System. They are described in detais as follows:

• Cognitive Radio Systems

Because the mobile location and tracking becomes important in user location, mobile tracking, security and military targeting, National Security Committee of Europe, Japan and USA have required mobile should have the location function in future. Therefore, the design of mobile location and tracking becomes an important topic in wireless communication network and has much potential in commercial product. In the urban area, due to complex environment such as shadowing, channel fading and non-line of sight, there exist severe interferences in mobile location and tracking. It is a challenge for robust design of mobile location and tracking. Further, in wireless sensor network there are still some problems like target location, power allocation and lifetime.

Therefore, we integrate some techniques such as fuzzy estimation, Markov jumping, extended kalman filter, data fusion, convex optimization and multi-objective optimization to solve the difficult design problems such as robust location and tracking of mobile, and location detection, power allocation and lifetime of wireless sensor network.

• Cooperative and Secure Wireless Communications

Cooperative wireless communications have been recognized as a promising approach in enhancing communication reliability and communication ranges. In these systems, multiple users or multiple relays cooperate with each other to send a common message to the destination in order to achieve desired diversity gains and improved symbol error performance. However, this distributed system is vulnerable to information security (secrecy). In particular, the common message that needs to be distributed across the cooperative users may be subject to overhearing by some nearby passive eavesdroppers. It is also possible that there exist some (active) malicious users who intend to disturb the communication between the other normal users and the destination. In view of these issues, we will investigate the following advanced research topics in the subproject:

• (1) Devise advanced cooperative/distributed beamforming schemes that can enhance the quality-of-service of the destination user while preventing the eavesdroppers from receiving the transmitted information message. The cooperative users (relays) may jointly generate and transmit some jamming signals (called cooperative jamming) to degrade the reception performance of the eavesdroppers.
• (2) Devise robust cooperative schemes (e.g., robust beamforming) that are insensitive to the disturbance caused by the malicious users. Several attacking behaviors of the malicious users will be considered, e.g., random phase rotation and random noise jamming.
• (3) Devise advanced cooperative schemes that can identify the malicious users and can adapt/update the transmission strategies to maintain the quality-of-service of the destination users according to the attacking strategies used by the malicious users.
• Broadband Wireless Communications

In future wireless communications, the demand for multimedia streaming services is expected to increase dramatically. To meet this demand, a broadband wireless communication system must increase the transmission rate and enhance the bandwidth efficiency. The orthogonal frequency division multiplexing (OFDM) technology is a promising solution for future broadband wireless communications because of its high bandwidth efficiency and superior resistance to multipath interference. Being standardized by the 3GPP (3rd Generation Partnership Project) community, LTE (Long Term Evolution)/LTE-A (LTE-Advanced) is the newest radio access system technology based on OFDM, which aims at providing higher data rate services with lower latency. LTE/LTE-A can support not only fundamental telecommunication services but also interactive multimedia applications, and thus will become the main stream of broadband wireless communications in the future.

There are still numerous unsolved problems related to the OFDM technology and future broadband wireless communication systems, including

Bandwidth-efficient modulation schemes can increase the available data rate and reduce the transmission latency; while energy-efficient modulation schemes can reduce the energy consumption and improve the receiving performance. In future broadband wireless communications, modulation schemes with high bandwidth efficiency and energy efficiency are essential and worthy of studying thoroughly.

MBMS is an efficient approach to provide multimedia services in wireless communication systems. By sharing the same data stream, multiple interested users can acquire the desired service simultaneously, thereby reducing the consumption of radio resources. To support multiple multimedia streams in OFDM-based MBMS, efficient radio resource allocation is a key issue and worthy of studying in the future.

At present, high data services are only available for fixed or low-mobility users. In future broadband wireless communications, supporting high data services for high-mobility users is attractive and essential. To meet this demand, user mobility estimation techniques and inter-carrier interference (ICI) cancellation techniques for OFDM systems are vital for performance improvement. These topics are worthy of studying in detail.

• Coding Theory and System Optimization
• (1) Structured Low-Density Parity-Check Codes and Associated Encoding/Decoding Algorithms

Low-density parity-check (LDPC) codes have attracted considerable attention in recent years because they can achieve near-Shannon-limit performance. However, most methods of designing LDPC codes are based on random construction techniques; the lack of structure implied by this randomness presents serious disadvantages in terms of the large complexity of encoding and decoding. Therefore, design of structured LDPC block and convolutional codes with reduced decoding complexity is very important for practical applications.

For LDPC block codes, we will develop new algebraic constructions of quasi-cyclic (QC) LDPC codes with enlarged minimum distance for good error performance. New unequal error protection (UEP) schemes based on QC-LDPC codes will also be investigated for practical applications. For LDPC convolutional codes, we will study new algebraic constructions with guaranteed girth and corresponding efficient encoding/decoding algorithms.

• (2) Error Control for Network-Coded Transmission

Network coding can increase the achievable throughput in a network by allowing intermediate nodes not only to route but also to perform operations on the incoming data. It has been considered as a conceptual breakthrough for network transmission and receiving extensive interests from the academia and industry in recent years. There are two types of network coding problems, coherent and noncoherent, depending on the assumption whether the network topology is known. Both approaches are susceptible to transmission errors caused by noise, interference, or malicious jamming. Hence, the problem of error control for network-coded transmission is very interesting and important.

For coherent network coding, we will conduct a well-round investigation of performance evaluation of error control for noisy channel networks, starting from a simple network topology and then to a general network setting. For noncoherent network coding, we will perform further study of the rank-metric approach for error control. We will develop new types of rank-metric codes and corresponding decoding algorithms. We will also conduct probabilistic performance evaluation of the rank-metric approach for error control, which has not been done in the literature.

• (3) Advanced Non-coherent Coded Transmission Schemes for MIMO Communication Systems

Both centralized and distributed multi-input multi-output (MIMO) systems are highly attractive due to their appealing capability of achieving large capacity over fading channels. For high mobility, the fading is rapid and the coherence interval is short. Consequently, non-coherent techniques, which avoid the use of pilots for channel estimation, can be adopted in MIMO systems designed for fast-fading channels. In order to increase the transmission reliability, powerful outer channel codes, such as turbo codes or low-density parity-check (LDPC) codes, can be serially concatenated with the inner MIMO mapper suitable for non-coherent detection. This research topic focuses on designing several advanced non-coherent coded communication systems, especially for channels with short coherence intervals.

• Communication IC

This research investigates the low-power cognitive radio SOC system, including cognitive radio SOC, low-power multi-standard FEC codec, and power-cognitive communication ICs.

This topic focuses on SoC for cognitive radio, which can provide much more spectrum efficiency than current communications under the techniques of spectrum sensing/detection and efficient spectrum management. There are many researches for algorithms and protocols; however, there are very few on the low complexity implementation and system integration solutions. Therefore, based on our preliminary studies and results on spectrum sensing/detection, we will continue to focus on more robust spectrum sensing/detection algorithms, low complexity/high power efficiency DSP, efficient spectrum management, and interference cancellation techniques. Combined with the developed SoC demo platform, we can provide high performance algorithms, SoCs and platform demo by the end of the project.

All the wireless transmission standards such as cellular, broadcast, and connectivity standards employ FEC (forward error correction) coding scheme in order to achieve high transmission reliability over noisy channel. In the future, multi-standard FEC codecs (encoders and decoders) will become key components for a variety of products such as mobile phones, portable entertainment and notebooks. Since CMOS scaling predicted by Moore’s law has significantly slowed down, in this research topic, we will use algorithmic and architectural approaches to design multi-standard FEC codec that achieves throughput values of Gb/s with lower complexities and power consumption levels within tight budgets imposed by the battery capacity.

The research of power-cognitive communication chip investigates to design the communication chip or manage the power-consumption of the chip according to the channel state information, including analog front-end circuits and baseband signal processing circuits.

• (a) Analog front-end circuits: In the unlicensed band, the RF circuits can determine whether some mixing circuits are operated or not according to the spectrum occupation status so as to reduce power consumption.
• (b) Baseband signal processing circuits: The spectrum occupation status or the channel quality determine the algorithm for baseband signal processing. Then, we can design the baseband digital signal processing circuits using reconfigurable architecture so as to reduce power consumption.

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• Published: 02 April 2024

Wireless radiofrequency network of distributed microsensors

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Distributed sensing of a dynamic environment is typically characterized by the sparsity of events, such as neuronal firing in the brain. Using the brain as inspiration, an event-driven communication strategy is developed that enables the efficient transmission, accurate retrieval and interpretation of sparse events across a network of thousands of wireless microsensors.

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Leonard, M. K. et al. Large-scale single-neuron speech sound encoding across the depth of human cortex. Nature 626 , 593–602 (2024). This paper reports a wired large-scale brain–computer interface in a human.

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Musk, E. & Neuralink. An integrated brain-machine interface platform with thousands of channels. J. Med. Internet Res. 21 , e16194 (2019). This paper reports a large-scale brain–computer interface device involving multiple sensors connected to a separate radio module.

Liu, S.-C. & Delbruck, T. Neuromorphic sensory systems. Curr. Opin. Neurobiol. 20 , 288–295 (2010). A paper that reports on dynamic vision cameras.

Indiveri, G. & Douglas, R. Neuromorphic vision sensors. Science 288 , 1180–1190 (2000). Another paper that reports on dynamic vision cameras.

Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro. 38 , 82–99 (2018). This paper reports developments in neuromorphic computing.

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This is a summary of: Lee, J. et al. An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors. Nat. Electron . https://doi.org/10.1038/s41928-024-01134-y (2024).

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Latest Thesis Topics in Wireless Communication

     Latest Thesis Topics in Wireless Communication is one of the marvelous platforms to provide our inventive ideas to select highly advanced research topics in most popular networking areas. Nowadays, we have 100+ highly experienced experts who are experts in the evergreen research field of wireless communication. For this reason, we can easily implement any complicated wireless communication projects. Our incredible Wireless Communication service is initiated with the vision of sharing our innovative ideas for students and research colleagues to achieve the best career in this competitive world. Day by day, we have updated our knowledge from the world’s top journals.

We provide the best guidance for you to develop your incredible research. Today, millions of students and research scholars are utilizing our Wireless Communication from various countries in-universe. For more guidance, you can send your queries or call us at 24/7.

Topics in Wireless Communication

    Latest Thesis Topics in Wireless Communication offers high tech advanced research topics for you to accomplish your dream of ground breaking research with the best achievements. We provide comprehensive support also for you to prepare your wireless communication research thesis with high standards.

These days, we have accomplished thousands of highly sophisticated wireless communication projects in a wide range of recently popular network research areas such as  wireless sensor networks, delay-tolerant networks, heterogeneous networks, green networking and also in energy harvesting, wireless ad hoc and also in mesh networks, software-defined networks, cognitive radio networks, wireless body areas sensor networks, underwater sensor networks, vehicular communication networks, cloud computing, fog computing, green computing, MIMO and also in Multi-Antenna Communications, etc . Let’s also have a glance over some of the important aspects of wireless communication.

Upcoming Research in Wireless Communication

• Neuromorphic Computing
• Novel Architectures for Optical Switches and also Routers
• Multi Domain Routing Protocols also for IP Over Optical Networks
• Performance and Spectrum Management in Cognitive Networks
• Multimedia Communication Via Cognitive Networks
• Traffic Engineering in Multi-Technology Networks
• Social abd Biometric Data Aware Adaptation
• Multi Level Loop Encapsulation in Smart Systems
• Regulatory Strategies on Spectrum Allocation also for Future Broadband Networks
• Facilitate SDR Technology also for Cognitive Radio
• Self-Organizing Socio-Technical Systems
• Cloud Computing and also Software Defined Network / Network Function Virtualization (SDN/NFV)
• Simulation Methodology for Communication Networks
• Satellite Technologies also for E-Learning
• Autonomous Mobile Robot Interaction
• Cloud Computing and also LTE Pro4.5
• Managing 5G LTE Advanced Networks and also LTE Heterogeneous Networks
• Mobile App also for Public Cloud
• Enterprise Centric Cloud Computing

Major Issues in Wireless Communication

• Reliability and Ownership Issues
• Energy Consumption Issues also based on Wireless Communication
• Big Data Analytics in Clouds
• Signal Coexistence, and also Data Rate on Wireless Communication
• Fairness Issues in Mobility and also Adaptive Management
• Security and Privacy also in Cloud Environment
• Propagation Issues also in Vehicular Sensor
• Legal and Regulatory Issues also in Security system
• Mobility Issues
• Complex Resource Allocation also in Modern Cellular Networks
• Cooperative Spectrum Sensing Problem also in Cognitive Radio
• Interference Management Problem also in Heterogeneous Networks

Major Tools for Wireless Communication

• – Visual Programming Tools
• – Emerging Telecommunication software Tools
• Divert Traffic
• And also in Heterogeneous Grooming Optical Network Simulator

Network Troubleshooting Tools

• Traceroute Tool
• SNMP Monitoring Tools:

              -NNMi tool

              -SolarWinds Network Performance Monitor tool

              -CA Spectrum Tool

• Centralized Log Management Tools:

              -Garylog Tool

              -Splunk

• NetFlow Analytics Tools

              -SevOne’s Tool

              -Acrutinizer Tool

Thesis Topics in Wireless Communication

• Digital Watermarking Based Information Integration and also in Protected Smart Grid Communications in Wireless Sensor Networks
• Personalized Quality of Experience (QoE) Management also Using Data Driven Architecture in 5G Wireless Networks
• Timer Division Duplex Operation also Using Sub-frame Scheduling Data Allocation in Packet Based Wireless Communication System
• User Profile Based Targeted Information Delivery also Using Novel Method and system in a Mobile Communication
•  Picocell Communication in a Macrocell also Using Controlling Uplink Power in Wireless Communications
• Gateway and Sensor Node Mutually Computing also in Wireless Sensor Network
• Q Controllable Antenna also for Wide Area Communication and Sensing in Wireless Charging through Coupled Magnetic Resonances
• Carrier Aggregation also for Apparatus of Transmitting Random Access Response and Configuring Downlink Timing in Mobile Communication

       We also aforesaid some of the interesting information about wireless communication such as upcoming research ideas, challenges, supported tools and also in latest topics. Do you aspire to acquire more knowledge from us? You can also approach us through online and also offline services at 24 hours.                

Get your Latest Thesis Topics in Wireless Communication…………

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• 15 Latest Networking Research Topics for Students

Comparative analysis between snort and suricata IDS software(s)

Description of the topic

The main focus of this research is to conduct a comparative analysis between Snort and Suricata software to determine which IDS software can provide better performance. There are various IDS software(s) available that can be used by organizations but it is difficult to identify which one is best (Aldarwbi et al., 2022). Different organizational structures are often facing problems while setting up an IDS system which results in false positives and intrusions. Through this research, it can be identified which IDS software is better and what secure configuration is required to detect intrusions (Waleed et al., 2022).

Research objectives

• To evaluate Snort and Suricata IDS software(s) to determine the most optimal one.
• To identify the false positive rate of Snort and Suricata on the networked environment.

Research questions

RQ1: Which IDS software can perform better on the production network in terms of performance, security, scalability and reliability?

RQ2: What different ways can be followed to deal with false positive problems in IDS technology?

Research methodology

The given research objectives and research questions can be addressed using quantitative research methodology where an experimental approach can be followed. For the given topic, both Snort and Suricata IDS systems should be configured and tested against different attacks. Depending on the findings, it can be analyzed which IDS software can perform better in terms of performance and security (Shuai & Li, 2021).

• Aldarwbi, M.Y., Lashkari, A.H. and Ghorbani, A.A. (2022) “The sound of intrusion: A novel network intrusion detection system,” Computers and Electrical Engineering , 104, p. 108455.
• Shuai, L. and Li, S. (2021) “Performance optimization of Snort based on DPDK and Hyperscan,” Procedia Computer Science , 183, pp. 837-843.
• Waleed, A., Jamali, A.F. and Masood, A. (2022) “Which open-source ids? Snort, Suricata or Zeek,” Computer Networks , 213, p. 109116.

Role of honeypots and honey nets in network security

Network Security has become essential nowadays and there is a need for setting up robust mechanisms to maintain confidentiality and integrity (Feng et al., 2023). Due to the number of security mechanisms available, organizations found it hard to finalize and implement them on their network. For example, honey pots and honeynet approaches look almost the same and have the same purpose but work differently. Under this research topic, the configuration of honeynets and honeypots can be done to check which one can perform better security in terms of trapping cyber attackers. The entire implementation can be carried out in the cloud-based instance for improved security and it can be identified which type of honey pot technology must be preferred (Maesschalck et al., 2022).

• To set up a honey pot system using Open Canary on the virtual instance to protect against cyber attackers.
• To set up a honeynet system on the virtual instance to assure protection is provided against malicious attackers.
• To test honeypots and honeynets by executing DDoS attacks to check which can provide better security.

RQ1: Why is there a need for using honeypots over honey pots in a production networked environment?

RQ2: What are the differences between cloud-based and local honey pot systems for endpoint protection?

This research can be carried out using the quantitative method of research. At the initial stage, the implementation of honeypots and honeypots can be done on the virtual instance following different security rules. Once the rules are applied, the testing can be performed using a Kali Linux machine to check whether honey pots were effective or honeynets (Gill et al., 2020).

• Feng, H. et al. (2023) “Game theory in network security for Digital Twins in industry,” Digital Communications and Networks [Preprint].
• Gill, K.S., Saxena, S. and Sharma, A. (2020) “GTM-CSEC: A game theoretic model for cloud security based on ids and Honeypot,” Computers & Security , 92, p. 101732
• Maesschalck, S. et al. (2022) “Don’t get stung, cover your ICS in honey: How do honeypots fit within industrial control system security,” Computers & Security , 114, p. 102598.

How do malware variants are progressively improving?

This research can be based on evaluating how malware variants are progressively improving and what should be its state in the coming future. Malware is able to compromise confidential user’s information assets which is why this research can be based on identifying current and future consequences owing to its improvements (Deng et al., 2023). In this field, there is no research work that has been carried out to identify how malware variants are improving their working and what is expected to see in future. Once the evaluation is done, a clear analysis can also be done on some intelligent preventive measures to deal with dangerous malware variants and prevent any kind of technological exploitation (Tang et al., 2023).

• To investigate types of malware variants available to learn more about malware's hidden features.
• To focus on future implications of malware executable programs and how they can be avoided.
• To discuss intelligent solutions to deal with all malware variants.

RQ1: How do improvements in malware variants impact enterprises?

RQ2: What additional solutions are required to deal with malware variants?

In this research, qualitative analysis can be conducted on malware variants and the main reason behind their increasing severity. The entire research can be completed based on qualitative research methodology to answer defined research questions and objectives. Some real-life case studies should also be integrated into the research which can be supported by the selected topic (Saidia Fasci et al., 2023).

• Deng, H. et al. (2023) “MCTVD: A malware classification method based on three-channel visualization and deep learning,” Computers & Security , 126, p. 103084.
• Saidia Fasci, L. et al. (2023) “Disarming visualization-based approaches in malware detection systems,” Computers & Security , 126, p. 103062.
• Tang, Y. et al. (2023) “BHMDC: A byte and hex n-gram based malware detection and classification method,” Computers & Security , p. 103118.

Implementation of IoT - enabled smart office/home using cisco packet tracer

The Internet of Things has gained much more attention over the past few years which is why each enterprise and individual aims at setting up an IoT network to automate their processes (Barriga et al., 2023). This research can be based on designing and implementing an IoT-enabled smart home/office network using Cisco Packet Tracer software. Logical workspace, all network devices, including IoT devices can be used for preparing a logical network star topology (Elias & Ali, 2014). To achieve automation, the use of different IoT rules can be done to allow devices to work based on defined rules.

• To set up an IoT network on a logical workspace using Cisco Packet Tracer simulation software.
• To set up IoT-enabled rules on an IoT registration server to achieve automation (Hou et al., 2023).

RQ: Why is the Cisco packet tracer preferred for network simulation over other network simulators?

At the beginning of this research, a quantitative research methodology can be followed where proper experimental set-up can be done. As a packet tracer is to be used, the star topology can be used to interconnect IoT devices, sensors and other network devices at the home/office. Once a placement is done, the configuration should be done using optimal settings and all IoT devices can be connected to the registration server. This server will have IoT rules which can help in achieving automation by automatically turning off lights and fans when no motion is detected (Baggan et al., 2022).

• Baggan, V. et al. (2022) “A comprehensive analysis and experimental evaluation of Routing Information Protocol: An Elucidation,” Materials Today: Proceedings , 49, pp. 3040–3045.
• Barriga, J.A. et al. (2023) “Design, code generation and simulation of IOT environments with mobility devices by using model-driven development: Simulateiot-Mobile,” Pervasive and Mobile Computing , 89, p. 101751.
• Elias, M.S. and Ali, A.Z. (2014) “Survey on the challenges faced by the lecturers in using packet tracer simulation in computer networking course,” Procedia - Social and Behavioral Sciences , 131, pp. 11–15.
• Hou, L. et al. (2023) “Block-HRG: Block-based differentially private IOT networks release,” Ad Hoc Networks , 140, p. 103059.

Comparative analysis between AODV, DSDV and DSR routing protocols in WSN networks

For wireless sensor networks (WSN), there is a major need for using WSN routing rather than performing normal routines. As WSN networks are self-configured, there is a need for an optimal routing protocol that can improve network performance in terms of latency, jitter, and packet loss (Luo et al., 2023). There are often various problems faced when WSN networks are set up due to a lack of proper routing protocol selection. As a result of this, severe downtime is faced and all links are not able to communicate with each other easily (Hemanand et al., 2023). In this research topic, the three most widely used WSN routing protocols AODV, DSDV and DSR can be compared based on network performance. To perform analysis, three different scenarios can be created in network simulator 2 (Ns2).

• To create three different scenarios on ns2 software to simulate a network for 1 to 100 seconds.
• To analyze which WSN routing is optimal in terms of network performance metrics, including latency, jitter and packet loss.
• To use CBR and NULL agents for all wireless scenarios to start with simulation purposes.

RQ: How do AODV, DSR and DSDV routing protocols differ from each other in terms of network performance?

This research can be carried out using a quantitative research method. The implementation for the provided research topic can be based on Ns2 simulation software where three different scenarios can be created (AODV, DSDV and DSR). For each scenario, NULL, CSR and UDP agents can be done to start with simulation for almost 1 to 100 seconds. For all transmissions made during the given time, network performance can be checked to determine which routing is best (Mohapatra & Kanungo, 2012).

• Human and, D. et al. (2023) “Analysis of power optimization and enhanced routing protocols for Wireless Sensor Networks,” Measurement: Sensors , 25, p. 100610. Available at: https://doi.org/10.1016/j.measen.2022.100610.
• Luo, S., Lai, Y. and Liu, J. (2023) “Selective forwarding attack detection and network recovery mechanism based on cloud-edge cooperation in software-defined wireless sensor network,” Computers & Security , 126, p. 103083. Available at: https://doi.org/10.1016/j.cose.2022.103083.
• Mohapatra, S. and Kanungo, P. (2012) “Performance analysis of AODV, DSR, OLSR and DSDV routing protocols using NS2 Simulator,” Procedia Engineering , 30, pp. 69–76. Available at: https://doi.org/10.1016/j.proeng.2012.01.835.

Securing wireless network using AAA authentication and WLAN controller

Wireless networks often face intrusion attempts due to insecure protocols and sometimes open SSIDs. As a result of this, man-in-the-middle and eavesdropping attacks become easier which results in the loss of confidential information assets (Sivasankari & Kamalakkannan, 2022). When it comes to managing networks in a large area, there are higher chances for attacks that enable cyber attackers in intercepting ongoing communication sessions. However, there is currently no research conducted where the use of AAA authentication has been done with WLAN controllers to make sure a higher level of protection is provided (Nashwan, 2021). The proposed research topic can be based on securing wireless networks with the help of AAA authentication and WLAN controllers. The use of AAA authentication can be done to set up a login portal for users whilst the WLAN controller can be used for managing all wireless access points connected to the network (Nashwan, 2021).

• To set up AAA authentication service on the wireless network simulated on Cisco Packet Tracer for proper access control.
• To set up a WLAN controller on the network to manage all wireless access points effortlessly.
• To use WPA2-PSK protocol on the network to assure guest users are only able to access wireless networks over a secure protocol.

RQ1: What additional benefits are offered by AAA authentication on the WLAN networks?

RQ2: Why are wireless networks more likely to face network intrusions than wired networks?

This research topic is based on the secure implementation of a wireless LAN network using a Cisco packet tracer. Hence, this research can be carried out using a quantitative research method. The implementation can be carried out using AAA authentication which can assure that access control is applied for wireless logins. On the other hand, a WLAN controller can also be configured which can ensure that all WAPs are managed (ZHANG et al., 2012).

• Nashwan, S. (2021) “AAA-WSN: Anonymous Access Authentication Scheme for wireless sensor networks in Big Data Environment,” Egyptian Informatics Journal , 22(1), pp. 15–26.
• Sivasankari, N. and Kamalakkannan, S. (2022) “Detection and prevention of man-in-the-middle attack in IOT network using regression modeling,” Advances in Engineering Software , 169, p. 103126.
• ZHANG, J. et al. (2012) “AAA authentication for Network mobility,” The Journal of China Universities of Posts and Telecommunications , 19(2), pp. 81-86.

OWASP's approach to secure web applications from web application exploits

The research can revolve around the development of web applications by considering OWASP's top 10 rules. Usually, web applications are deployed by organizations depending on their requirements and these applications are vulnerable to various exploits, including injection, broken authentication and other forgery attacks (Poston, 2020). Identifying every single vulnerability is difficult when reference is not taken and often organizations end up hosting a vulnerable server that leads to privacy issues and compromises confidential information easily. In this research, OWASP's top 10 approaches can be followed to develop a secure web application that can be able to protect against top web application exploits. This approach is based on emphasizing severe and minor vulnerabilities which must be patched for protecting against web application attacks (Deepa & Thilagam, 2016).

• The first objective can be setting up an insecure web application on the cloud environment which can be exploited with different techniques.
• The second objective can be to consider all techniques and procedures provided by OWASP's top 10 methodologies.
• The last objective can be applying all fixes to insecure web applications to make them resistant to OWASP top 10 attacks (Sonmez, 2019).

RQ1: What are the benefits of using OWASP's top 10 approaches to harden web applications in comparison to other security approaches?

The research methodology considered for this research project can be quantitative using an experimental approach. The practical work can be done for the selected topic using AWS or the Azure cloud platform. Simply, a virtual web server can be configured and set up with a secure and insecure web application. Following OWASP's top 10 techniques and procedures, the web application can be secured from possible attacks. In addition, insecure applications can also be exploited and results can be evaluated (Applebaum et al., 2021).

• Applebaum, S., Gaber, T. and Ahmed, A. (2021) “Signature-based and machine-learning-based web application firewalls: A short survey,” Procedia Computer Science , 189, pp. 359–367. Available at: https://doi.org/10.1016/j.procs.2021.05.105.
• Deepa, G. and Thilagam, P.S. (2016) “Securing web applications from injection and logic vulnerabilities: Approaches and challenges,” Information and Software Technology , 74, pp. 160–180. Available at: https://doi.org/10.1016/j.infsof.2016.02.005.
• Poston, H. (2020) “Mapping the owasp top Ten to the blockchain,” Procedia Computer Science , 177, pp. 613-617. Available at: https://doi.org/10.1016/j.procs.2020.10.087.
• Sonmez, F.Ö. (2019) “Security qualitative metrics for Open Web Application Security Project Compliance,” Procedia Computer Science , 151, pp. 998-1003. Available at: https://doi.org/10.1016/j.procs.2019.04.140.

Importance of configuring RADIUS (AAA) server on the network

User authentication has become significant nowadays as it guarantees that a legitimate user is accessing the network. But a problem is faced when a particular security control is to be identified for authentication and authorization. These controls can be categorized based on mandatory access controls, role-based access control, setting up captive portals and many more. Despite several other security controls, one of the most efficient ones is the RADIUS server (SONG et al., 2008). This server can authenticate users on the network to make sure network resources are accessible to only legal users. This research topic can be based on understanding the importance of RADIUS servers on the network which can also be demonstrated with the help of the Cisco Packet Tracer. A network can be designed and equipped with a RADIUS server to ensure only legal users can access network resources (WANG et al., 2009).

• To configure RADIUS (AAA) server on the network which can be able to authenticate users who try to access network resources.
• To simulate a network on a packet tracer simulation software and verify network connectivity.

RQ1: What are other alternatives to RADIUS (AAA) authentication servers for network security?

RQ2: What are the common and similarities between RADIUS and TACACS+ servers?

As a logical network is to be designed and configured, a quantitative research methodology can be followed. In this research coursework, a secure network design can be done using a packet tracer network simulator, including a RADIUS server along with the DMZ area. The configuration for the RADIUS server can be done to allow users to only access network resources by authenticating and authorizing (Nugroho et al., 2022).

• Nugroho, Y.S. et al. (2022) “Dataset of network simulator related-question posts in stack overflow,” Data in Brief , 41, p. 107942.
• SONG, M., WANG, L. and SONG, J.-de (2008) “A secure fast handover scheme based on AAA protocol in Mobile IPv6 Networks,” The Journal of China Universities of Posts and Telecommunications , 15, pp. 14-18.
• WANG, L. et al. (2009) “A novel congestion control model for interworking AAA in heterogeneous networks,” The Journal of China Universities of Posts and Telecommunications , 16, pp. 97-101.

Comparing mod security and pF sense firewall to block illegitimate traffic

Firewalls are primarily used for endpoint security due to their advanced features ranging from blocking to IDS capabilities and many more. It is sometimes challenging to identify which type of firewall is best and due to this reason, agencies end up setting up misconfigured firewalls (Tiwari et al., 2022). This further results in a cyber breach, destroying all business operations. The research can be emphasizing conducting a comparison between the two most widely used firewalls i.e. Mod Security and pF sense. Using a virtualized environment, both firewalls can be configured and tested concerning possible cyber-attacks (Lu & Yang, 2020).

• To use the local environment to set up Mod security and pF sense firewall with appropriate access control rules.
• To test both firewalls by executing distributed denial of service attacks from a remote location.
• To compare which type of firewall can provide improved performance and robust security.

RQ: How do Mod security and pF sense differ from each other in terms of features and performance?

The practical experimentation for both firewalls can be done using a virtualized environment where two different machines can be created. Hence, this research can be carried out using a quantitative research method . The first machine can have Mod security and the second machine can have pF sense configured. A new subnet can be created which can have these two machines. The third machine can be an attacking machine which can be used for testing firewalls. The results obtained can be then evaluated to identify which firewall is best for providing security (Uçtu et al., 2021).

• Lu, N. and Yang, Y. (2020) “Application of evolutionary algorithm in performance optimization of Embedded Network Firewall,” Microprocessors and Microsystems , 76, p. 103087.
• Tiwari, A., Papini, S. and Hemamalini, V. (2022) “An enhanced optimization of parallel firewalls filtering rules for scalable high-speed networks,” Materials Today: Proceedings , 62, pp. 4800-4805.
• Uçtu, G. et al. (2021) “A suggested testbed to evaluate multicast network and threat prevention performance of Next Generation Firewalls,” Future Generation Computer Systems , 124, pp. 56-67.

Conducting a comprehensive investigation on the PETYA malware

The main purpose of this research is to conduct a comprehensive investigation of the PETYA malware variant (McIntosh et al., 2021). PETYA often falls under the category of ransomware attacks which not only corrupt and encrypt files but can compromise confidential information easily. Along with PETYA, there are other variants also which lead to a security outage and organizations are not able to detect these variants due to a lack of proper detection capabilities (Singh & Singh, 2021). In this research, a comprehensive analysis has been done on PETYA malware to identify its working and severity level. Depending upon possible causes of infection of PETYA malware, some proactive techniques can also be discussed (Singh & Singh, 2021). A separation discussion can also be made on other malware variants, their features, and many more.

• The main objective of this research is to scrutinize the working of PETYA malware because a ransomware attack can impact the micro and macro environment of the organizations severely.
• The working of PETYA malware along with its source code can be reviewed to identify its structure and encryption type.
• To list all possible CVE IDs which are exploited by the PETYA malware.

RQ1: How dangerous is PETYA malware in comparison to other ransomware malware?

This research can be based on qualitative research methodology to evaluate the working of PETYA malware from various aspects, the methodology followed and what are its implications. The research can be initiated by evaluating the working of PETYA malware, how it is triggered, what encryption is applied and other factors. A sample source code can also be analyzed to learn more about how cryptography is used with ransomware (Abijah Roseline & Geetha, 2021).

• Abijah Roseline, S. and Geetha, S. (2021) “A comprehensive survey of tools and techniques mitigating computer and mobile malware attacks,” Computers & Electrical Engineering , 92, p. 107143.
• McIntosh, T. et al. (2021) “Enforcing situation-aware access control to build malware-resilient file systems,” Future Generation Computer Systems , 115, pp. 568-582.
• Singh, J. and Singh, J. (2021) “A survey on machine learning-based malware detection in executable files,” Journal of Systems Architecture , 112, p. 101861.

Setting up a Live streaming server on the cloud platform

Nowadays, various organizations require a live streaming server to stream content depending upon their business. However, due to a lack of proper hardware, organizations are likely to face high network congestion, slowness and other problems (Ji et al., 2023). Referring to the recent cases, it has been observed that setting up a streaming server on the local environment is not expected to perform better than a cloud-based streaming server configuration (Martins et al., 2019). This particular research topic can be based on setting up a live streaming server on the AWS or Azure cloud platform to make sure high network bandwidth is provided with decreased latency. The research gap analysis would be conducted to analyze the performance of live streaming servers on local and cloud environments in terms of network performance metrics (Bilal et al., 2018).

• To set up a live streaming server on the AWS or Azure cloud platform to provide live streaming services.
• To use load balancers alongside streaming servers to ensure the load is balanced and scalability is achieved.
• To use Wireshark software to test network performance during live streaming.

RQ1: Why are in-house streaming servers not able to provide improved performance in comparison to cloud-based servers?

RQ2: What additional services are provided by cloud service providers which help in maintaining network performance?

The implementation is expected to carry out on the AWS cloud platform with other AWS services i.e. load balancer, private subnet and many more (Efthymiopoulou et al., 2017). Hence, this research can be carried out using a quantitative research method. The configuration of ec2 instances can be done which can act as a streaming server for streaming media and games. For testing this project, the use of OBS studio can be done which can help in checking whether streaming is enabled or not. For network performance, Wireshark can be used for testing network performance (George et al., 2020).

• Bilal, KErbad, A. and Hefeeda, M. (2018) “QoE-aware distributed cloud-based live streaming of multi-sourced Multiview Videos,” Journal of Network and Computer Applications , 120, pp. 130-144.
• Efthymiopoulou, M. et al. (2017) “Robust control in cloud-assisted peer-to-peer live streaming systems,” Pervasive and Mobile Computing , 42, pp. 426-443.
• George, L.C. et al. (2020) “Usage visualization for the AWS services,” Procedia Computer Science , 176, pp. 3710–3717.
• Ji, X. et al. (2023) “Adaptive QoS-aware multipath congestion control for live streaming,” Computer Networks , 220, p. 109470.
• Martins, R. et al. (2019) “Iris: Secure reliable live-streaming with Opportunistic Mobile Edge Cloud offloading,” Future Generation Computer Systems , 101, pp. 272-292.

Significance of using OSINT framework for Network reconnaissance

Network reconnaissance is becoming important day by day when it comes to penetration testing. Almost all white hat hackers are dependent on the OSINT framework to start with network reconnaissance and footprinting when it comes to evaluating organizational infrastructure. On the other hand, cyber attackers are also using this technique to start fetching information about their target. Currently, there is no investigation carried out to identify how effective the OSINT framework is over traditional reconnaissance activities (Liu et al., 2022). This research is focused on using OSINT techniques to analyze victims using different sets of tools like Maltego, email analysis and many other techniques. The analysis can be based on fetching sensitive information about the target which can be used for conducting illegal activities (Abdullah, 2019).

• To use Maltego software to conduct network reconnaissance on the target by fetching sensitive information.
• To compare the OSINT framework with other techniques to analyze why it performs well.

RQ1: What is the significance of using the OSINT framework in conducting network reconnaissance?

RQ2: How can the OSINT framework be used by cyber hackers for conducting illegitimate activities?

The OSINT framework is easily accessible on its official website where different search options are given. Hence, this research can be carried out using a quantitative research method. Depending upon the selected target, each option can be selected and tools can be shortlisted for final implementation. Once the tools are shortlisted, they can be used to conduct network reconnaissance (González-Granadillo et al., 2021). For example, Maltego can be used as it is a powerful software to fetch information about the target.

• Abdullah, S.A. (2019) “Seui-64, bits an IPv6 addressing strategy to mitigate reconnaissance attacks,” Engineering Science and Technology , an International Journal, 22(2), pp. 667–672.
• Gonzalez-Granadillo, G. et al. (2021) “ETIP: An enriched threat intelligence platform for improving OSINT correlation, analysis, visualization and sharing capabilities,” Journal of Information Security and Applications , 58, p. 102715.
• Liu, W. et al. (2022) “A hybrid optimization framework for UAV Reconnaissance Mission Planning,” Computers & Industrial Engineering , 173, p. 108653.

Wired and wireless network hardening in cisco packet tracer

At present, network security has become essential and if enterprises are not paying attention to the security infrastructure, there are several chances for cyber breaches. To overcome all these issues, there is a need for setting up secure wired and wireless networks following different techniques such as filtered ports, firewalls, VLANs and other security mechanisms. For the practical part, the use of packet tracer software can be done to design and implement a highly secure network (Sun, 2022).

• To use packet tracer simulation software to set up secure wired and wireless networks.
• Use different hardening techniques, including access control rules, port filtering, enabling passwords and many more to assure only authorized users can access the network (Zhang et al., 2012).

RQ: Why is there a need for emphasizing wired and wireless network security?

Following the quantitative approach, the proposed research topic implementation can be performed in Cisco Packet Tracer simulation software. Several devices such as routers, switches, firewalls, wireless access points, hosts and workstations can be configured and interconnected using Cat 6 e cabling. For security, every device can be checked and secure design principles can be followed like access control rules, disabled open ports, passwords, encryption and many more (Smith & Hasan, 2020).

• Smith, J.D. and Hasan, M. (2020) “Quantitative approaches for the evaluation of Implementation Research Studies,” Psychiatry Research , 283, p. 112521.
• Sun, J. (2022) “Computer Network Security Technology and prevention strategy analysis,” Procedia Computer Science , 208, pp. 570–576.
• Zhang, YLiang, R. and Ma, H. (2012) “Teaching innovation in computer network course for undergraduate students with a packet tracer,” IERI Procedia , 2, pp. 504–510.

Different Preemptive ways to resist spear phishing attacks

When it comes to social engineering, phishing attacks are rising and are becoming one of the most common ethical issues as it is one of the easiest ways to trick victims into stealing information. This research topic is based on following different proactive techniques which would help in resisting spear phishing attacks (Xu et al., 2023). This can be achieved by using the Go-Phish filter on the machine which can automatically detect and alert users as soon as the phished URL is detected. It can be performed on the cloud platform where the apache2 server can be configured along with an anti-phishing filter to protect against phishing attacks (Yoo & Cho, 2022).

• To set up a virtual instance on the cloud platform with an apache2 server and anti-phishing software to detect possible phishing attacks.
• To research spear phishing and other types of phishing attacks that can be faced by victims (Al-Hamar et al., 2021).

RQ1: Are phishing attacks growing just like other cyber-attacks?

RQ2: How effective are anti-phishing filters in comparison to cyber awareness sessions?

The entire research can be conducted by adhering to quantitative research methodology which helps in justifying all research objectives and questions. The implementation of the anti-phishing filter can be done by creating a virtual instance on the cloud platform which can be configured with an anti-phishing filter. Along with this, some phishing attempts can also be performed to check whether the filter works or not (Siddiqui et al., 2022).

• Al-Hamar, Y. et al. (2021) “Enterprise credential spear-phishing attack detection,” Computers & Electrical Engineering , 94, p. 107363.
• Siddiqui, N. et al. (2022) “A comparative analysis of US and Indian laws against phishing attacks,” Materials Today: Proceedings , 49, pp. 3646–3649.
• Xu, T., Singh, K. and Rajivan, P. (2023) “Personalized persuasion: Quantifying susceptibility to information exploitation in spear-phishing attacks,” Applied Ergonomics , 108, p. 103908.
• Yoo, J. and Cho, Y. (2022) “ICSA: Intelligent chatbot security assistant using text-CNN and multi-phase real-time defense against SNS phishing attacks,” Expert Systems with Applications , 207, p. 117893.

Evaluating the effectiveness of distributed denial of service attacks

The given research topic is based on evaluating the effectiveness of distributed denial of service attacks on cloud and local environments. Hence, this research can be carried out using a quantitative research method. Cyber attackers find DDoS as one of the most dangerous technological exploitation when it comes to impacting network availability (Krishna Kishore et al., 2023). This research can revolve around scrutinizing the impact of DDoS attacks on the local environment and cloud environment. This can be done by executing DDoS attacks on a simulated environment using hoping or other software(s) to check where it has a higher magnitude (de Neira et al., 2023).

• To set up a server on the local and cloud environment to target using DDoS attacks for checking which had experienced slowness.
• To determine types of DDoS attack types, their magnitude and possible mitigation techniques.

RQ: Why do DDoS attacks have dynamic nature and how is it likely to sternly impact victims?

The experimentation for this research can be executed by creating a server on the local and cloud environment. Hence, this research can be carried out using a quantitative research method. These servers can be set up as web servers using apache 2 service. On the other hand, a Kali Linux machine can be configured with DDoS execution software. Each server can be targeted with DDoS attacks to check its effectiveness (Benlloch-Caballero et al., 2023).

• Benlloch-Caballero, P., Wang, Q. and Alcaraz Calero, J.M. (2023) “Distributed dual-layer autonomous closed loops for self-protection of 5G/6G IOT networks from distributed denial of service attacks,” Computer Networks , 222, p. 109526.
• de Neira, A.B., Kantarci, B. and Nogueira, M. (2023) “Distributed denial of service attack prediction: Challenges, open issues and opportunities,” Computer Networks , 222, p. 109553.
• Krishna Kishore, P., Ramamoorthy, S. and Rajavarman, V.N. (2023) “ARTP: Anomaly-based real time prevention of distributed denial of service attacks on the web using machine learning approach,” International Journal of Intelligent Networks , 4, pp. 38–45.

Recommended Readings

Latest Web Development Research Topics

Top Management Research Topics

Newest AI Research Topics

Research in every field is becoming more and more essential because of constant developments around the world. Similar is the case in the field of networking. This is the reason; students who are preparing to master the field of networking need to keep their knowledge of the current state of the art in the field up to date.

However, choosing the right research topic often becomes a tough task for students to carry out their research effectively. That being the case, this list contains 15 latest research topics in the field of networking. Whether you are a seasoned researcher or just starting, this list can provide you with ample inspiration and guidance to drive your research forward in the dynamic and evolving field of Networking.

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Wireless communication is a process of conveying data among multiple devices without a direct wired link . In this technology, radio waves are very frequently used as a wireless communication medium. A wideband mobile wireless network offers a huge volume of multimedia information which allows communication with everyone everywhere. This page is about to present the most interesting research ideas and latest wireless communication project topics , standards and technologies!!!

In the current era of wireless communication, more mechanisms are coming up to improve the adeptness in spectrum transmission and utilization . Also, it is focused to use “everything over IP” for effortlessly interrelating varied structured networks. 

Why Wireless Communication?

On top of mobility, wireless networks provide adaptability and reliability over any kind of network for easy use . As a result, it ultimately acquires widespread familiarity in a short period. For instance , smartphones are featured with a significantly high throughput presentation.  

Similarly, infrastructure also plays a major role in wireless technology characterization. In comparison with a wireless system, the wired system setup is a more costly and time-intensive process . In the case of undesired conditions or distant situations, wireless communication suits more perfectly rather than wired communication. Now let’s see about the important characteristics of wireless interaction.

Key Features of Wireless Technologies 

• LOS or NLOS transmission
• Unlicensed free spectrum 
• Moderate coverage and mobility 
• LoS/NloS transportations
• Minimum deployment cost
• Unlicensed free spectrum
• Large data rate 
• Complete mobility 
• LOS/NLOS communications 
• Huge coverage
• Assured QoS
• LOS/NLOS communications
• Average coverage
• Assured Quality of Service
• Moderate mobility 
• LoRa communication
• Line of Sight / Non-Line of Sight communication
• Performance does not relies on snow, fog, and dust

To guarantee the standard of our research undertakings, we continuously update our skills with recent technological innovations . From this study, we are acquainted with so many interesting facts about wireless technologies with their performance factors. For example, we have given SigFox, LoRaWAN, LTE Network , LTE-M, and NB-IoT .

Performance of Latest Wireless Technologies 

• Modulation:  CSS,  Band:  Sub-GHz ISM: EU (433 MHz, 868 MHz), US (915 MHz), Asia (430 MHz),  Data Rate:  03-37.5 kbps (LoRA), 50 kps (FSK),  Range:  5 km (urban) , 15 km (rural),  MAC:  pure ALOHA,  Topology:  star of stars,  Payload size:  up to 250 B,  Proprietary aspects:  PHY layer
• Modulation:  UNB DBPSK, GFSK,  Band:  Sub-GHz ISM: EU (868 MHz), US (902 MHz),  Data Rate:  100 bps (UL), 600 bps (DL),  Range:  10 km (urban) , 50 km (rural),  MAC:  pure ALOHA,  Topology:  star,  Payload size:  12 B (UL), 8 B (DL),  Proprietary aspects:  PHY and MAC layers
• Modulation:  QPSK,  Band:  Licensed 700-900 MHz,  Data Rate:  158 kbps (UL), 106 kbps (DL),  Range:  15 km,  MAC:  FDMA / OFDMA,  Topology:  star,  Payload size:  125 B (UL), 85 B (DL),  Proprietary aspects:  Full stack
• Modulation:  16QAM,  Band:  Licensed 700-900 MHz,  Data Rate:  1 Mbps,  Range:  11 km,  MAC:  FDMA / OFDMA,  Topology:  star,  Payload size:  Unknown,  Proprietary aspects:  Full stack

Similarly, we have also listed down the recent wireless standards with their performance features. For instance, we have given IEEE 802.15.4k, IEEE 802.15.4g, Weightless-W, and Weightless-N.

Performance of Latest Wireless Standards 

• Modulation:  MR-(FSK, OFDMA, OQPSK),  Band:  ISM Sub-GHz & 2.4 GHz,  Data Rate:  4.8 kbps-800 kbps,  Range:  10 km,  MAC:  CSMA / CA,  Topology:  star, mesh, peer-to-peer,  Payload size:  2047 B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private
• Modulation:  DSSS, FSK,  Band:  ISM Sub-GHz & 2.4 GHz,  Data Rate:  1.5 bps-128 kbps,  Range:  5 km (urban),  MAC:  CSMA / CA or ALOHA with PCA,  Topology:  star,  Payload size:  2047 B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private
• Modulation:  UNB DBPSK,  Band:  ISM Sub-GHz EU (868 MHz), US (915 MHz),  Data Rate:  30 bps-100 kbps,  Range:  3 km (urban),  MAC:  slotted ALOHA,  Topology:  star,  Payload size:  20 B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private
• Modulation:  16-QAM, BPSK, QPSK, DBPSK,  Band:  TV white spaces 470-790 MHz,  Data Rate:  1 kbps-10 mbps,  Range:  5 km (urban),  MAC:  TDMA / FDMA,  Topology:  star,  Payload size:  >10B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private

In addition, our experts have listed out few main Wireless Communication Project Topics that help scholars to get a clear vision about the current research. We provide best dissertation help in wireless communication . We have supported countless research scholars.

10+ Latest Wireless Communication Project Topics

• Radio Frequency and Microwave Technologies 
• Advance RF Antenna and Propagation 
• Advance Microwaves, Microwave devices, and Components
• Multiple Cross-Layer Mac Design
• Wireless Data Communications and Computing
• Improved Equalization, Diversity, Channel Codding Techniques 
• Integration of Cognitive radio with Dynamic spectrum access 
• RF-Energy Harvesting with Massive Wireless Energy Transfer
• Full-Duplex Radio Communication and Technologies
• Wireless Heterogeneous Cellular Networks Theory 
• Massive MIMO based mmWave communication Model
• Adaptive Design, Modulation, and coding for wireless systems
• Radio Propagation and Radio channel characterization 
• Resource-Aware Allocation and load –Aware Balancing 
• MIMO based Adaptive Space-Time Processing 

Energy-efficient wireless communications

In specific, we have discussed energy-efficient Wireless Communication Project Topics which gain more importance in recent research. As matter of fact, it is deployed in various smart grid systems like meter power line observation, data acquisition, and resource demand management. In this, it can also be used in several sections of the smart grid such as SG-NAN, SG-WAN, and SG-HAN . As well as, it catches the relations among the following aspects in the radio frequency transceiver,

• Order of Modulation 
• Power Consumption
• Channel fading
• Power Amplifier 
• Distance of Transceiver 
• other Circuit Modules 

Research Ideas in Wireless Communications 

• Multi-Attribute based Vertical Handover Solution 
• Strategy for Network Switching
• Power Control in Wireless Transmission
• Integrated Cluster-based Routing Protocol 
• Topology Optimization for Directional Antenna Network

Also, we are ready to share a few more important updates about the wireless communication trends. So if you are looking for the best Wireless Communication Project Topics, you can find us as the best solution to carry over your research career.

• Our Promise
• Our Achievements
• Our Mission
• Proposal Writing
• System Development
• Paper Writing
• Paper Publish
• Synopsis Writing
• Thesis Writing
• Assignments
• Survey Paper
• Conference Paper
• Journal Paper
• Empirical Paper
• Journal Support
• PhD Research Topics in Wireless Communication

In fact,  the growing technologies like “IoT, IoE, WoT” are working upon communications .   PhD research topics in wireless communication  is a good service for you. As of now,  5G & 6G  is the best domain for all young researchers.

Come to us not only for guidance but also for the ‘Massive Success’…

Brilliant PhD Research Topics in Wireless Communication

• SDN/NFV for 5G network
• Information/content-centric networking
• Cognitive radio for wireless communication
• Multi-user multiple input multiple output antenna designs
• Cooperative, device-to-device, multi-hop communication
• Millimeter-wave and also sub-THz communication
• Social communication in delay tolerant networks
• Power line communication in grid computing
• Cloud/fog radio access network for backhaul networks
• IoT network virtualization
• Integrated networking platforms (SDN-VANET, SD-CRN, and also CR-WSN)

Reach our expert panel team to know more interesting wireless research topics .

Besides, we offer all of your needs within the first meet. In light of our help, you can search for an ideal road for you for latest 5g communication research ideas . Thus, you will reach your research goal without harm.

Probably, we turn all of your dreams into achievement in front of the world…

Extra-Ordinary Simulation Tools

• Visual System
• And also GNS-3

PhD Research topics in wireless communication

On the whole, you will gain your “ Proposal, Experimentation, Paper, and Thesis or Dissertation Writing ” services from us. In short, you will get outright support from our experts for PhD research topics in wireless communication .

To this end, when you are wavering in your research, join us!!! We are always here to turn your ‘CANT into CAN’!!!

Best 20 project notions in Wireless Communication,

An effective function of Transmit Power Optimization designed for a Hybrid PLC/VLC/RF Communication System

A new method for Opportunities of Optical Spectrum used for Future Wireless Communications

An effectual design process of Research and Development of Customized Wireless Device Based on Multimode Chip for Energy Internet Applications

An effective Risk-Sensitive Approach for Ultra Reliable Communication in 5G mmWave Networks

A new methodology for Wireless Optical and Fiber-optic Underwater Cellular, Hybrid Acoustic in Mobile Communication Networks

An inventive source of Secrecy Rate of MISO Optical Wireless Scattering Communications system

A new system of Wireless communications with programmable metasurface for Transceiver design and experimental results

An innovative source of  Wireless Optical Communication based on Different Seawater Environments for Performance Analysis in LDPC Codes

An effectual function of Throughput Maximization used Hybrid Backscatter Supported for Cognitive Wireless Powered Radio Networks

An inventive solution of general channel model for visible light communications in underground mines scheme

A Creative mechanism for Joint Data-Energy Beamforming and Traffic Offloading in Cloud Radio Access Networks With Energy Harvesting-Aided device to device Communications

An effectual process of Relay Assisted in Cooperative Communication for WSNs

Innovative methods for Optical Wireless Hybrid Networks used by 5G and Beyond Communications

An efficient source of Jammer Assisted in Legitimate Eavesdropping for Wireless Powered Suspicious Communication Networks

An original mechanism for Efficient, Review of Fast, and Bendable Radio Frequency Integrated Receivers intended for future of Wireless Communication Systems

A new technology based on Hybrid MAC for Low Latency Wireless Communication Enabling Industrial HMI uses scheme

An inventive mechanism for Infrared indoor wireless MIMO communication system used by 1.2GHz OOK modulation methods

On the use of Low Frequency Electromagnetic Communication based on Underwater Wireless Telemetry for Inland Waterways system

Design and development function of Application and Test of Wireless Communication Platform Based on 802.11 Protocols

An effectual function of Resource Allocation for Wireless-Powered IoT Networks With Short Packet Communication system

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

• Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
• Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.

CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

Client Reviews

I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it.

I had wishes to complete implementation using latest software/tools and I had no idea of where to order it. My friend suggested this place and it delivers what I expect.

It really good platform to get all PhD services and I have used it many times because of reasonable price, best customer services, and high quality.

My colleague recommended this service to me and I’m delighted their services. They guide me a lot and given worthy contents for my research paper.

I’m never disappointed at any kind of service. Till I’m work with professional writers and getting lot of opportunities.

- Christopher

Once I am entered this organization I was just felt relax because lots of my colleagues and family relations were suggested to use this service and I received best thesis writing.

I recommend phdservices.org. They have professional writers for all type of writing (proposal, paper, thesis, assignment) support at affordable price.

You guys did a great job saved more money and time. I will keep working with you and I recommend to others also.

These experts are fast, knowledgeable, and dedicated to work under a short deadline. I had get good conference paper in short span.

Guys! You are the great and real experts for paper writing since it exactly matches with my demand. I will approach again.

I am fully satisfied with thesis writing. Thank you for your faultless service and soon I come back again.

Trusted customer service that you offer for me. I don’t have any cons to say.

I was at the edge of my doctorate graduation since my thesis is totally unconnected chapters. You people did a magic and I get my complete thesis!!!

- Abdul Mohammed

Good family environment with collaboration, and lot of hardworking team who actually share their knowledge by offering PhD Services.

I enjoyed huge when working with PhD services. I was asked several questions about my system development and I had wondered of smooth, dedication and caring.

I had not provided any specific requirements for my proposal work, but you guys are very awesome because I’m received proper proposal. Thank you!

- Bhanuprasad

I was read my entire research proposal and I liked concept suits for my research issues. Thank you so much for your efforts.

- Ghulam Nabi

I am extremely happy with your project development support and source codes are easily understanding and executed.

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I had found this as a wonderful platform for scholars so I highly recommend this service to all. I ordered thesis proposal and they covered everything. Thank you so much!!!

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This is a campuswide week of events, lectures, and volunteer opportunities designed to educate and inspire action related to environmental justice, climate, and nature-based solutions. This year’s theme is Restore & Regenerate.

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Two Penn students awarded Truman Scholarships

Third-year students aravind krishnan and tej patel are both in the vagelos program in life sciences and management..

University of Pennsylvania third-year students, Aravind Krishnan and Tej Patel, have received Harry S. Truman Scholarships , a merit-based award of as much as $30,000 for graduate or professional school to prepare for careers in public service.

Krishnan and Patel are both majoring in molecular and cell biology, as well as health care management and policy and statistics, in the Vagelos Program in Life Sciences and Management, a dual-degree program in the Wharton School and the College of Arts and Sciences .

They are among 60 Truman Scholars from 54 U.S. colleges and universities selected this year from 709 candidates nominated by 285 institutions. They are the 31st and 32nd  Truman Scholars from Penn since the first awards in 1977.

Krishnan, from Newark, Delaware, co-founded ToxiSense , a biosensor technology for detecting bacterial toxin contamination and screening for bacterial infections. He conducts research in the Perelman School of Medicine ’s Greenberg Lab , where he works on understanding DNA damage repair mechanisms. He is development chair of the Shelter Health Outreach Program , a Penn undergrad-led organization that runs hypertension screening clinics across Philadelphia and partners with Penn Medicine to operate mobile primary care clinics and a free clinic in West Philadelphia. In that role, Krishnan also organized a citywide health needs assessment, interviewing over 100 people at shelters across Philadelphia. Based on the findings, he then helped launch a mobile dental clinic program with Penn Dental Medicine . He has also worked on equity in care delivery abroad, including at the Aravind Eye Hospital in India, through the Center for the Advanced Study of India. Krishnan is a Perry World House Student Fellow , U.N. Millennium Fellow , and recipient of the Wharton Social Impact Research Experience grant and Hassenfeld Foundation Social Impact Grant . He has received several awards and prizes, including the Perelman Grand Prize at the Penn Venture Lab Startup Challenge . He plans to pursue an M.D./Ph.D. focused on immunology and infectious disease.

Patel, from Billerica, Massachusetts, is interested in making health care systems more equitable and cost-effective. He has co-authored 19 peer-reviewed research manuscripts , 10 as first-author, on radiation oncology, health economics, and care delivery. Patel works in Penn Medicine 's Radiation Oncology and Breast Surgery departments, as well as the Human Algorithm Collaboration Lab , where he led a systemwide study examining the cost-effectiveness of a machine-learning intervention meant to increase serious illness conversations in end-of-life care. Patel co-founded the Social Equity Action Lab, a youth-led think tank that brings together students, institutional partners, and policymakers to inform legislation on key health care issues. On campus, Patel is the director of the Locust Bioventures group, coordinator for the Netter Center Pipeline Program , and policy/outcomes researcher for the Shelter Health Outreach Program . He also has interned with the Mongan Institute for Health Policy and Institute for Healthcare Improvement , working on projects covering Medicare Part D policy and alternative payment models. Patel plans to pursue an M.D./M.P.P. with a goal to improve nationwide care delivery.

The students applied to the Harry S. Truman Scholarship program with assistance from Penn’s Center for Undergraduate Research and Fellowships . 

In addition to funding, Truman Scholars also receive priority admission and supplemental financial aid at some graduate institutions, leadership training, and special internship opportunities within the federal government.

The Harry S. Truman Scholarship Foundation was created by Congress in 1975 to be the nation’s living memorial to President Truman. The foundation has a mission to select and support the next generation of public service leaders.

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‘The Illuminated Body’ fuses color, light, and sound

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25 years of ‘LOVE’

The iconic sculpture by pop artist Robert Indiana arrived on campus in 1999 and soon became a natural place to come together.

Two-and-a-half decades of research in Malawi

As the country’s life expectancy has risen, the Malawi Longitudinal Study of Families and Health has shifted its current and future research to aging.

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In hot water: Coral resilience in the face of climate change

Over a decade, researchers from Penn studied coral species in Hawaii to better understand their adaptability to the effects of climate change.

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Communication and Connectivity to Improve Sustainable Mobility

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Traffic mobility problems have major repercussions in terms of energy inefficiency and pollution in cities. Therefore, the climate challenge and public health require the adaptation of the transport system towards sustainable mobility, with a reduction in the use of private vehicles and a corresponding ...

Keywords : Advertising, Communication Campaigns, Intelligent Transport, Vehicle Connectivity, Planning Policies, Awareness-Raising, Behavioural Change, Sustainable Mobility

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