hot research topics in wireless communication

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.

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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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HW designed the survey and led the write up of the manuscript. XL contributed part of the writing of the manuscript. YD took part in the discussion of the work described in this paper. All authors have read and approved the manuscript.

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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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Special issue on Wireless communication systems in beyond 5G era

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During the development and deployment of 5G mobile cellular systems, a number of new technological concepts, advances and paradigm shifts have emerged, altering the perspective of research community on how one should design wireless communication systems in the future. The proliferation of machine learning and artificial intelligence tools and technologies, while having limited effect on 5G, are already demonstrating their imminent future impact on the design of communication systems across the layers of the traditional communication protocol architecture. Most notably, these technologies further accelerate the trends of cognition and self-organization, ranging from the device spectrum access level, across algorithms governing physical and medium access layer operation, all the way to the level of network organization and resource allocation. ​​​​ In addition, advent of new materials, combined with their controllability and programmability, transforms the propagation environment from a passive entity into an active communication system ingredient, especially in the domain of high-frequency (e.g., THz-domain) wireless communications based on directed and pencil-beam signal propagation. In another development, the ever-increasing densification of cellular infrastructure is gradually escaping the Earth surface and we are witnessing introduction of the third, aerial dimension where dense deployments will firstly emerge at a very low-height level using Unmanned Aerial Vehicles (UAVs), such as drones, and Low-Earth Orbit (LEO)-level using micro-satellite constellations, creating novel challenges in 3D network design and optimization.  Next, going to the domain of miniaturization and wireless sensor platforms, progress from on-body to in-body sensors is offering not only further prospects of creating novel human-machine interfaces that go beyond the existing trends of virtual and augmented reality, but promise future impact on biomedical research, diagnostics, and therapeutics.  The question of energy efficient communication technologies operating at network-wide scales to address raising global energy consumption challenges and climate change concerns and, at the opposite end of the energy consumption spectrum, wireless power transfer technologies for the deployment of self-sustainable and battery-less IoT sensors, are expected to create significant impact on future wireless system design.  Finally, overlaid on the potentials of the technology evolution as described above, lies the key question: What are the future services and applications for which we need to design novel beyond 5G wireless communication systems?  ​This special issue is dedicated to exploration of future and evolving technologies that are likely to have significant impact on the design of wireless communication systems in the beyond 5G era.​

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• 3D networks of terrestrial, airborne and satellite communication systems
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• Network softwarization and virtualization in beyond 5G era
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Wireless Networking

The ability to connect anywhere and anytime has led to the increasing use of wireless communication in homes, in offices, on the roads, and almost everywhere. data intensive applications such as streaming media and two-way video-chat have further fueled the need for higher data-rates. several faculty in our group have multiple projects related to wireless networking. our focus is to design reliable, robust, higher data-rate, spectrally efficient, and secure network services and architectures for tomorrow. some of the key research topics studied are as follows: full-duplex wireless communication, resource allocation, mobility, medium access control, vehicular networks, wireless local-area networks, cognitive radios, cellular networks, sensor and actuator networks, cross-layer design, scaling laws, and wireless security., .cls-1{fill:#a91e22;}.cls-2{fill:#c2c2c2;} double-arrow faculty.

Recent and Emerging Topics in Wireless Industrial Communications: A Selection

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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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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

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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Correspondence to Muhammad Alam .

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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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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
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• Moderate mobility 
• LoRa communication
• Line of Sight / Non-Line of Sight communication
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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.

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• Radio Frequency and Microwave Technologies 
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• Adaptive Design, Modulation, and coding for wireless systems
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• 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 
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Research Ideas in Wireless Communications 

• Multi-Attribute based Vertical Handover Solution 
• Strategy for Network Switching
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• Topology Optimization for Directional Antenna Network

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

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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

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