what is channel assignment strategies

Channel Assignment Strategies in Mobile Communication Explained

Mohammad Jamiu

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What are channel assignment strategies.

Channel assignment strategies in mobile communication are used to allocate available radio channels to mobile users in a way that maximizes spectrum utilization and minimizes interference.

This is important because the radio spectrum is a limited resource, and there is a growing demand for mobile communication services.

Types of Channel Assignment Strategies

There are three main types of channel assignment strategies:

Fixed channel assignment (FCA)

Dynamic channel assignment (dca), hybrid channel assignment (hca).

In fixed channel assignment, each cell is allocated a fixed or predetermined set of channels (voice channels).

If all channels in a cell are occupied, the call from a mobile user is blocked and the user won’t receive service.

This strategy is simple to implement, but it can lead to inefficient spectrum utilization and increased interference if the traffic load is not evenly distributed across the cells.

Advantages of FCA:

• Simple to implement and manage
• Reduces co-channel interference

Disadvantages of FCA:

• Can lead to inefficient spectrum utilization
• Can lead to increased call blocking if traffic load is not evenly distributed across the cells

In dynamic channel assignment, channels are assigned to cells on demand, based on the current traffic load. i.e., there is no allocation of predetermined set of channels (voice channels).

This strategy is more efficient than FCA, but it is also more complex to implement.

Advantages of DCA:

• Improves spectrum utilization
• Reduces likelihood of blocking since all available channels are accessible to all cells

Disadvantages of DCA:

• More complex to implement and manage than FCA
• Can lead to increased call blocking if traffic load is high

HCA is a combination of FCA and DCA. In HCA, each cell is allocated a fixed set of channels, but additional channels can be dynamically assigned to cells if needed.

This strategy offers a good balance between simplicity and efficiency.

Advantages of HCA:

• Improves spectrum utilization compared to FCA
• Simpler to implement and manage than DCA

Disadvantages of HCA:

• Can be more complex to implement than FCA
• Can lead to increased co-channel interference compared to DCA

Channel Borrowing

Channel borrowing is a technique that can be used with any channel assignment strategy.

In channel borrowing, a cell can borrow a channel from a neighboring cell if all of its own channels are occupied.

This process is carried out by the Mobile Switching Center (MSC) which supervises the borrowing procedures and ensures that the borrowing of a channel does not interrupt or interfere with any of the calls in progress in the donor cell.

This technique can help to reduce call blocking and improve spectrum utilization.

Advantages of channel borrowing:

• Reduces call blocking

Disadvantages of channel borrowing:

• Can increase co-channel interference

Comparison of Channel Assignment Strategies (FCA, DCA and HCA)

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Channel Assignment Strategies

What are channel allocation strategies.

Channel Allocation is a method of assigning the existing channels to the various cells within the cellular system. Channel assignment is mainly done to manage the traffic demands of the cell.

Concept of Channel Assignment

We are aware of the basic idea involved in mobile communication that a radio signal path is maintained between the base station and the mobile station so as to maintain communication. We know that there are various multiple access technique like FDMA, TDMA, and CDMA.

In Frequency Division Multiple Access, the various users of the cell are allotted different frequency bands to establish communication. While in Time Division Multiple Access, the communication is maintained over the same frequency band at different time slots. As against, in Code Division Multiple Access a well-defined pattern code is imposed on the actual signal and then transmission over the channel takes place.

Basically, mobile communication is known to be a wireless network that allows users to connect while roaming.

Need for Channel Assignment

We are aware of the fact that there are number of radio channels in a cell and when a call is made then a specific radio channel is assigned to ensure communication. However, when call attempts in a cell increases more than the assigned radio channels, then the extra call requests will remain unassigned, and hence the call will get dropped. When a higher number of calls remain unconnected within the cell then this shows poor quality service of the provider.

There is a term called ‘blocking probability’ which is defined as the overall dropped calls as a percentage of overall call requests within the cell.

Generally, service providers ensure that the blocking probability must be less than 3% of each cell and this depends on the number of channels assigned to each cell.

The figure shown above clearly represents that the number of channels assigned is 3 but the number of requested calls is 5. So, here number of calls is more than the overall available channels and so there are 2 dropped calls.

Now, let us have a look at the figure shown below:

Here in this scenario, the number of assigned channels to the cell are more in comparison to overall call requests generated and thus here some of the channels remain unused. This turns out to be a waste of the available channel resources.

Hence channel assignment is necessarily done to ensure good usage efficiency within the channel.

Considerations

There are few considerations regarding the channel assignment which are as follows:

• The antenna at the base station must be accessible but all devices of the channel hence spacing of the channels must be proper.
• The traffic demand of the cell must be fulfilled by all channels of that cell.
• Cochannel interference must be least as much as possible.

It is to be noted here that in earlier days practically economical approach was that a single antenna must be used for both transmitter and receiver. However, with the advent of technology now it is not necessary to use a common antenna as we can use power-efficient common amplifiers.

Hence, to achieve stable channel assignment point second and third must be more severely considered than the first one.

Methods of Channel Assignment

Channel Assignment is basically done to avoid co-channel and adjacent channel interference in the cellular system as much as possible.

There are mainly two methods of assigning channels to each cell which are fixed channel assignment and dynamic channel assignment. Let us now proceed to understand each method separately.

Fixed Channel Assignment

It is abbreviated as FCA. In this channel assignment technique, each mobile subscriber within the cell is assigned its own frequency channel to ensure communication. Here basically what follows is assigning the voice channels for a long period of time. The channel numbering is done in increasing order manner according to frequency. No matter what is the overall number of channels in the channel set, the highest and the lowest channel sets are frequency adjacent to each other.

Basically, in the fixed channel assignment technique, the channels to the cell are assigned statically. This means that whatever the traffic demand is the assigned channels will not vary. Hence this method is suitable for uniform traffic load conditions within the cell.

The figure below represents fixed channel assignment allocation:

One can determine the number of channels for a specific cell by the formulation given below:

: N corresponds to the number of overall channels needed to cover a specific coverage area

D is the frequency reuse distance and

R denotes the radius of the cell

This channel assignment offers maximal frequency reuse which is known to be its crucial advantageous factor. However, at the same time, major disadvantageous factor is that channel bandwidth gets wasted and congestion may be a problem when the cell traffic is non-uniform.

Therefore, if we want fixed channel assignment then it is necessary that the expected load in each cell must be estimated along with that the required number of channels must be calculated.

Dynamic Channel Assignment

In this channel assignment technique, the allocated channels to a cell is not static and thus varies depending on the requirement. Here the channel assignment is time-varying in nature and changes according to the traffic demands in the cell. It is abbreviated as DCA and sometimes called the dynamic method.

This method shows suitability in cases when the traffic demand is non-uniform. There is a resource pool that provides the channel to the cells according to the requirement of each cell.

The figure shown above clearly indicates that the channels a, b and c are used by various cells but according to the requirement as we have not preassigned the channels to the cells.

This method is known to be a comparatively more efficient one than FCA because it offers flexibility in changing the demands of the system. Day by day, with the increasing demand of mobile phone users, this approach fulfills today’s needs.

If we talk about the advantage offered by this method then it is known that the channel usage efficiency is quite high. But at the same time the disadvantageous factor is that here randomization exists thus frequency reuse is not maximized. Also, as the process involves real time computation thus, is time-consuming and complex.

It is to be noted here that in this method, once the cell demand gets fulfilled by the channel then the channel gets returned to the pool for further reusability.

The pool from where channels are demanded can be of centralized or distributed.

This is all about channel assignment strategies.

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Channel Assignment Schemes

Contributed by John S. Davis, II , U.C. Berkeley

One main issue in cellular system design reduces to one of economics. Essentially we have a limited resource transmission spectrum, that must be shared by several users. Unlike wired communications which benefits from isolation provided by cables, wireless users within close proximity of one another can cause significant interference to one another. To address this issue, the concept of cellular communications was introduced around in 1968 by researchers at AT&T Bell Labs. The basic concept being that a given geography is divided into polygons called cells.

Each cell is allocated a portion of the total frequency spectrum. As users move into a given cell, they are then permitted to utilize the channel allocated to that cell. The virtue of the cellular system is that different cells can use the same channel given that the cells are separated by a minimum distance according to the system propagation characteristics; otherwise, intercellular or cochannel interference occurs. The minimum distance necessary to reduce cochannel interference is called the reuse distance. The reuse distance is defined as the ratio of the distance, D , between cells that can use the same channel without causing interference and the cell radius, R . Note that R is the distance from the center of a cell to the outermost point of the cell in cases when the cells are not circular.

Channel Allocation

• Fixed Channel Allocation,
• Dynamic Channel Allocation and
• Hybrid Channel Allocation which is a combination of the first two methods.

Fixed Channel Allocation

Dynamic channel allocation.

• First, DCA methods typically have a degree of randomness associated with them and this leads to the fact that frequency reuse is often not maximized unlike the case for FCA systems in which cells using the same channel are separated by the minimum reuse distance.
• Secondly, DCA methods often involve complex algorithms for deciding which available channel is most efficient. These algorithms can be very computationally intensive and may require large computing resources in order to be real-time.

Hybrid Channel Allocation Schemes

The third category of channel allocation methods includes all systems that are hybrids of fixed and dynamic channel allocation systems. Several methods have been presented that fall within this category and in addition, a great deal of comparison has been made with corresponding simulations and analyses [Cox, Elnoubi, Jiang, Katzela, Yue, Zhang]. We will present several of the more developed hybrid methods below.

Channel Borrowing is one of the most straightforward hybrid allocation schemes. Here, channels are assigned to cells just as in fixed allocation schemes. If a cell needs a channel in excess of the channels previously assigned to it, that cell may borrow a channel from one of its neighboring cells given that a channel is available and use of this channel won't violate frequency reuse requirements. Note that since every channel has a predetermined relationship with a specific cell, channel borrowing (without the extensions mentioned below) is often categorized as a subclass of fixed allocation schemes. The major problem with channel borrowing is that when a cell borrows a channel from a neighboring cell, other nearby cells are prohibited from using the borrowed channel because of co-channel interference. This can lead to increased call blocking over time. To reduce this call blocking penalty, algorithms are necessary to ensure that the channels are borrowed from the most available neighboring cells; i.e., the neighboring cells with the most unassigned channels.

• The ratio of fixed to dynamic channels varies with traffic load.
• Nominal channels are ordered such that the first nominal channel of a cell has the highest priority of being applied to a call within the cell.

The last nominal channel is most likely to be borrowed by neighboring channels. Once a channel is borrowed, that channel is locked in the co-channel cells within the reuse distance of the cell in question. To be "locked" means that a channel can not be used or borrowed. Zhang and Yum [Zhang] presented the BDCL scheme as an improvement over the BCO method. From a frequency reuse standpoint, in a BCO system, a channel may be borrowed only if it is free in the neighboring cochannel cells. This criteria is often too strict.

• In Borrowing with Directional Channel Locking, borrowed channels are only locked in nearby cells that are affected by the borrowing. This differs from the BCO scheme in which a borrowed channel is locked in every cell within the reuse distance. The benefit of BDCL is that more channels are available in the presence of borrowing and subsequent call blocking is reduced. A disadvantage of BDCL is that the statement "borrowed channels are only locked in nearby cells that are affected by the borrowing" requires a clear understanding of the term "affected." This may require microscopic analysis of the area in which the cellular system will be located. Ideally, a system can be general enough that detailed analysis of specific propagation measurements is not necessary for implementation.

A natural extension of channel borrowing is to set aside a portion of the channels in a system as dynamic channels with the remaining (nominal) channels being fixed to specified cells. If a cell requires an extra channel, instead of borrowing the channel from a neighboring cell, the channel is borrowed from the common "bank" of dynamic channels. An important consideration in hybrid systems of this type is the ratio of dynamic channels to fixed channels. Analysis by Cox and Reudlink [Cox - 1973] showed that given ten channels per cell, an optimum ratio was 8 fixed channels and 2 dynamic channels. In general, the optimum ratio depends upon the traffic load [Zhang]. In addition to BDCL, a second channel allocation method was presented by Yum and Zhang [Zhang]. Referred to as Locally Optimized Dynamic Assignment Strategy (LODA), this method is best described as a purely dynamic channel allocation procedure as opposed to a hybrid method. In this strategy there are no nominal channels; all channels are dynamic. When a given cell needs to accommodate a call, it chooses from among the bank of available channels according to some cost criteria. The channel with minimum cost is assigned. In a general sense, the cost is a measure of the future blocking probability in the vicinity of the cell given that the candidate channel is assigned. A more detailed description of the cost function will be addressed below.

Dynamic Channel Reassignment

Similar to the goals of dynamic channel assignment is the process of Dynamic Channel Reassignment (DCR). Whereas a DCA scheme allocates a channel to an initial call or handover , a DCR system switches a cell's channel (that is currently being used) to another channel which is closer to the optimum according to frequency reuse or other cost criteria. Thus, for example, a user communicating with channel n may be switched to channel m during the middle of her/his call if channel m is a more efficient use of the available bandwidth from a frequency reuse point of view. Philosophically, DCR is equivalent to DCA.

Simulation and Comparison of Channel Allocation Schemes

A great deal of work is available comparing various realizations of channel allocation schemes [Cox, Elnoubi, Jiang, Katzela, Yue, Zhang]. In comparing performance, typical system metrics include blocking probability of new calls and blocking probability of handover calls. These metrics are written as functions of offered traffic (where the traffic may be written in a variety of forms). It is generally assumed that a blocked new call is preferred over a blocked hand-off call. The idea being that with a blocked hand-off, users are forced to terminate communication in the middle of their session. If this blocking happens at a particularly inopportune time, the results could be disastrous (e.g., business partners cut off in the middle of a vital negotiation). In the case of a blocked new call, at least the business negotiation hasn't started and the involved parties aren't interrupted. Blocking probability is an important metric throughout the field of queueing theory and in the case of M/M/ 1 queues, the Erlang-B formula is often used for analysis of blocking probability. Because blocked calls can be very disconcerting, systems are typically designed to have blocking probabilities of no more than 1% or 2%. This is consistent with the assumption of small offered traffic loads.

Cox and Reudink were the first researchers to present published comparisons of different channel allocation schemes. Their comparison was based on simulation of an outdoor vehicular wireless communication system [Cox - 1971, Cox - 1972, Jakes]. The simulation divided a region into a grid of square cells. The movement of vehicles had a two dimensional normal distribution with 0 mean and 30 mph standard deviation in each of the two orthogonal directions. Poisson arrivals were assumed for the rate of calls per vehicle and call durations were assume to have a truncated normal distribution (truncated on the left at zero) with a "mean" 90 seconds (true mean of 103.5 seconds).

Cox and Reudink's study considered uniform and non-uniform distributions of spatial traffic. In the uniform case, all cells had approximately the same call arrival rate while in the non-uniform case, some cells had a significantly higher call arrival rate. With both the uniform and non-uniform spatial distributions, fixed channel allocation schemes were optimally matched so that the cells with the greatest numbers of calls had the greatest number of channels to deal with those calls. In both cases of uniform and non-uniform traffic, results showed that for low blocking probabilities, dynamic channel allocation schemes could handle more calls than fixed channel allocation schemes. More specifically, in the case of uniform traffic, the DCA approach outperformed the FCA approach when the blocking probability was lower than 10%. At a blocking probability of 1%, the DCA approach could handle over 10% more calls than the FCA approach. In the case of non-uniform traffic, the DCA approach outperformed FCA for blocking rates up to 60%. At a blocking rate of 1%, DCA could handle almost 70% more calls per cell than FCA. Cox and Reudink performed another comparison involving dynamic channel reassignment in [Cox - 1973]. In this hybrid procedure, the total number of available channels is broken into two groups: fixed and dynamic channels. When a cell requires a channel, it first searches for an available fixed channel that is preassigned to the cell. If none of the fixed channels are available, a dynamic channel is searched for from the common bank of dynamic channels. If this search is in vain, the call is blocked. When users who were assigned fixed channels end their calls, these freed fixed channels are then assigned to users in the same cell who are currently using dynamic channels. This frees the dynamic channel for future use and ensures that a large number of channels being used are the optimally-spaced, fixed channels. Results from Cox and Reudink's study of dynamic channel reassignment showed that channel use was increased by over 60% compared to fixed channel allocation for a blocking rate of 1%. This result corresponds to uniform offered traffic.

• Fixed Channel Assignment (FCA),
• Borrowing with Channel Ordering (BCO),
• Borrowing with Directional Channel Locking (BDCL) and
• Locally Optimized Dynamic Assignment (LODA).

With respect to uniform offered traffic, their results showed that BDCL had the lowest blocking probability followed by BCO, LODA and FCA. With non-uniform offered traffic, the relative performance of the four methods was the same with the exception that in this case, LODA performed better than BCO. It makes sense that the ordering for BDCL, BCO and FCA was as found. Indeed, BDCL was specifically designed as an improvement over BCO and BCO was designed as an improvement over FCA [Zhang, Elnoubi]. The fact that the performance of LODA varies under uniform versus non-uniform traffic is rather interesting however. The reason behind this phenomenon is that LODA provides optimal channel allocation only in local regions. Given non-uniform traffic which consists of dense regions in certain local areas, LODA will accommodate these regions of high traffic offering. However, in a global sense, the LODA algorithm will not necessarily provide the optimal allocation. With uniform offered traffic, LODA does not have any regions with peak traffic to optimize; i.e., no local regions within which the benefits of LODA can be realized. Furthermore, with respect to the entire region, the optimization is generally not optimal in a global sense. The result is that with uniform traffic, LODA does not have any advantage to offer over BCO. From the previous discussion we see that one general result of all of the comparisons is that dynamic channel allocation outperforms fixed channel allocation for low blocking rates (below 10% in most cases). Blocking rates above 1% or 2% are generally not tolerated. This is generally an accepted guideline throughout the telecommunications industry and we will adhere to this design constraint as well.

Common Principles of Channel Allocation Schemes

• Channel allocation schemes must not violate minimum frequency reuse conditions.
• Channel allocation schemes should adapt to changing traffic conditions.
• Channel allocation schemes should approach (from above) the minimum frequency reuse constraints so as to efficiently utilize available transmission resources.

As the first requirement suggests, all channel allocation schemes adhere to condition 1. From a frequency reuse standpoint, a fixed channel allocation system distributes frequency (or other transmission) resources to the cells in an optimum manner; i.e., common channels are separated by the minimum frequency reuse distance. Thus, a fixed channel allocation scheme perfectly satisfies condition 3 as well. However, a fixed allocation scheme does not satisfy condition 2.

Philosophically, any dynamic channel allocation scheme will meet the requirements of all of the above three conditions to some degree. At the system architecture level dynamic channel allocation schemes may differ widely, but fundamentally, their only difference is in the degree to which they satisfy condition 3. Different DCA schemes attempt to satisfy condition 3 (in addition to conditions 1 and 2) by approaching the minimum frequency reuse constraint arbitrarily closely, and by doing so in as short a time period as possible. The above three conditions point to the fact that design of dynamic channel allocation schemes falls within the general class of optimization problems. Furthermore, since we can always assume that the available number of base stations is finite and the transmission resources will always be countable (due to FCC requirements if nothing else) then our problem can be reduced to the subclass of combinatorial optimization problems. As with all combinatorial optimization problems, there will exist a solution space and a cost function [Aarts & Korst]. A typical element of the solution space could be a particular layout of frequency channels among the base-stations. The cost function can be loosely characterized as the difference between the frequency reuse of an arbitrary solution and the frequency reuse of the optimized solution. The error associated with a non-optimized cost is realized as a future increased blocking probability or an otherwise unwarranted lack of channel availability. It is typically assumed that the solution to the wireless dynamic channel allocation problem is NP-complete [Yue, Cox - 1971]. The definition of np-completeness follows from the conjecture made in the late 1960's that there exists a class of combinatorial optimization problems of such inherent complexity that any algorithm, solving each instance of such a problem to optimality, requires a computational effort that grows superpolynomially with the size of the problem. In the case of dynamic channel allocation, the complexity is generally attributed to the required inclusion of cochannel interference in any analysis of dynamic channel allocation schemes [Yue]. The author is aware of one published article to date offering an analytical method (approximate) for calculating the performance of dynamic channel allocation [see Yue]. Recently, several approximation techniques have been proposed as methods for solving condition 3 of the dynamic channel allocation problem. In particular there has been interest in applying simulated annealing techniques [Duque-Anton] and neural network methods [Chan, Kunz, Funabiki] to dynamic channel allocation.

Channel and Base-Station Allocation Schemes

• [1] Chan, P. T. H., Palaniswami, M. & Everitt, D., "Neural Network-Based Dynamic Channel Assignment for Cellular Mobile Communication Systems," IEEE Transactions on Vehicular Technology, vol. 43, no. 2, May 1994.
• [2] Cox, D.C. and Reudink, D.O., "Dynamic Channel Assignment in High-Capacity Mobile Communications Systems," Bell System Technical Journal, vol. 50, no. 6, July-August 1971.
• [3] Cox, D.C. and Reudink, D.O., "Dynamic Channel Assignment in Two-Dimensional Large -Scale Mobile Radio Systems," Bell System Technical Journal, vol. 51, no. 7, September 1972.
• [4] Cox, D.C. and Reudink, D.O., "Increasing Channel Occupancy in Large-Scale Mobile Radio Systems: Dynamic Channel Reassignment," IEEE Transactions on Communications, vol. 21, no. 11, November 1973.
• [5] Duque-Anton, M., Kunz, D., & Ruber, B., "Channel Assignment for Cellular Radio Using Simulated Annealing," IEEE Transactions on Vehicular Technology, vol. 42, no. 1, February 1993.
• [6] Elnoubi, S. M., Singh, R., and Gupta, S.C., "A New Frequency Channel Assignment Algorithm in High Capacity Mobile Communication Systems," IEEE Transactions on Vehicular Technology, vol. 31, no. 3, August 1982.
• [7] Funabiki N. & Takefuji, Y., "A Neural Network Parallel Algorithm for Channel Assignment Problems in Cellular Radio Networks," IEEE Transactions on Vehicular Technology, vol. 41, no. 4, November 1992.
• [8] Gamst, A., "Homogeneous Distribution of Frequencies in a Regular Hexagonal Cell System," IEEE Transactions on Vehicular Technology, vol. 31, no. 3, August 1982.
• [9] Jakes, W. C., Micr wave Mobile Communication ns, IEEE Press: New Jersey, c. 1993.
• [10] Jiang, H. and Rappaport, S. S., "CBWL: A New Channel Assignment and Sharihng Method for Cellular Communication Systems," IEEE Transactions on Vehicular Technology, vol. 43., no. 2, May 1994.
• [11] Katzela, I., "Channel Assignment Schemes for Cellular Mobile Telecommunication Systems," Unpublished work, Columbia University, December 15, 1992.
• [12] Kunz, D., "Channel Assignment for Cellular Radio Using Neural Networks," IEEE Transactions on Vehicular Technology, vol. 40, no. 1, February 1991.
• [13] Yue, W., "Analytical Methods to Calculate the Performance of a Cellular Mobile Radio Communication with Hybrid Channel Assignment," IEEE Transactions on Vehicular Technology, vol. 40, now. 2, May 1991.
• [14] Zhang, M. and Yum, TS. P., "Comparisons of Channel-Assignment Strategies in Cellular Mobile Telephone Systems", IEEE Transactions on Vehicular Technology, vol. 38, no. 4, November 1989.

Combinatorial Optimization

• [15] Aarts, E. and Korst, J., Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing, John Wiley & Sons: Chichester, c. 1989.
• [16] Garey, M. R. & Johnson, D. S., Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Company: New York, c. 1979.

Try you luck as a frequency assigner in the Dynamic Channel Allocation game .

JPL's Wireless Communication Reference Website � John S. Davis, II and 1993, 1995.

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Home > Books > Wireless Mesh Networks - Efficient Link Scheduling, Channel Assignment and Network Planning Strategies

Channel Assignment Schemes Optimization for Multi-Interface Wireless Mesh Networks Based on Link Load

Submitted: 16 December 2011 Published: 14 August 2012

DOI: 10.5772/46100

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1. Introduction

In recent years, wireless mesh networks (WMNs) were deployed as a type of next generation wireless broadband networks. WMNs provide wireless broadband accessibility to extend the Internet connectivity to the last mile and improve the network coverage. WMN consists of a set of mesh routers and mesh clients ( Fig. 1) . Mesh routers are usually stationary and form multi-hop wireless backbone network (i.e. mesh routers are interconnected with each other via wireless medium). Some or all of the mesh routers also serve as access points for mobile users ( mesh clients ) under their coverage. Usually one or more mesh routers have direct connections to wired network and serve as Internet gateways for the rest of the network. These nodes are called mesh gateways . Compared to traditional wireless LANs, the main feature of WMNs is their multi-hop wireless backbone capability ( Conti et al., 2007 ).

Traditionally, wireless networks are equipped with only one IEEE 802.11 radio interface. However, a single-interface inherently restricts the whole network by using only one single channel ( Fig. 3a) . In order to communicate successfully, two neighboring routers have to build a logical link which operates on a common channel. Due to that, all wireless nodes have to use only one radio interface, all logical links in network must use the same channel. If two neighboring links operate on the same channel and transfer data simultaneously, then they definitely interfere with each other. The network capacity and the performance may degrade significantly because of the interference ( Gupta & Kumar, 2000 ). The key factor for reducing the effect of interference is the using of non-overlapping channels (standard IEEE 802.11b/g provides 3 and standard IEEE 802.11a up to 12 non-overlapping channels) ( Ramachandran et al., 2006 ). In practice, IEEE 802.11b/g defines 11 communication channels (number of communication channels varies due to regulations of different countries) but only 3 of them are non-overlapping ( Fig.2) .

WMN architecture

Channel spectrum occupation in IEEE 802.11b/g

Using multiple non-overlapping channels in single interface network disconnects the subset of nodes using one channel from other nodes that are not using the same channel ( Fig. 3b) . For this reason this approach generally requires MAC layer modification and per packet channel switching capability for radio interfaces ( Marina & Das, 2005 ). Before every data transmission a channel selection mechanism evaluates the available channels and selects a channel to transmit. There are also some problems introduced with channel switching mechanism. These problems include multi-channel hidden terminal problem, broadcast problem, deafness problem and channel deadlock problem ( Raniwala et al., 2004 ).

One of the most promising approaches lies in using multiple radio interfaces and multiple non-overlapping channels ( Fig. 3c) . This solution is better than previous one, because of providing the effective usage of given frequency spectrum ( Conti et al., 2007 ). This architecture overcomes deficiencies of single interface solution. It allows using of multiple interfaces per node to allow the simultaneous transmission and reception on different radio interfaces tuned to different channels, which can essentially improve network capacity. However, the number of radio interfaces is always much higher than the number of effective channels, which causes an existence of many different links between mesh routers operating on the same channel. For this reason, the suitable channel assignment method is needed to maintain the connectivity between mesh nodes and to minimize the effect of interference ( Raniwala et al., 2004 ).

The channel assignment (CA) in a multi-interface WMN consists of a task to assign channels to the radio interfaces by such a way to achieve efficient channel utilization and to minimize the interference. The problem of optimally assigning channels in an arbitrary mesh topology has been proved to be NP-hard (non-deterministic polynomial-time hard) based on its mapping to a graph-coloring problem. Therefore, channel assignment schemes predominantly employ heuristic techniques to assign channels to radio interfaces belonging to WMN nodes.

The channel assignment algorithms can be divided into three main categories: fixed, dynamic and hybrid, depending on the frequency with which it is modified by the channel assignment scheme. In a fixed scheme, the CA is almost constant, while in a dynamic one it is continuously updated to improve performance. A hybrid scheme applies a fixed scheme for some radio interfaces and dynamic one for the others ( Yulong Chen et al., 2010 ).

Different types of WMNs

The main objective of this chapter is to give to reader the compact information about problems connected with optimal using of radio interfaces and radio channels in wireless mesh networks. The optimal using is computed from several different points of view, e.g. network topology, number of data flows, number of nodes by comparison of selected QoS parameters. In the second part of the chapter, the new proposed centralized channel assignment concept called First Random Channel Assignment algorithm (FRCA) is compared with two other channel assignment techniques (CCA, LACA) by the same QoS parameters.

The rest of this chapter is organized as follows. In section 2, the related work is summarized. In section 3, the methods and simulation results to find the optimal number of radio interfaces per node are introduced. In section 4 the mathematical background and graph based mathematical model is described and in the next section different types of channel assignment methods based on links load are analyzed. Section 6 concludes the chapter.

2. Related work

There exist a large number of studies which address the channel assignment problem in wireless mesh networks. Several works have proposed MAC protocols for utilizing multiple channels ( So & Vaidya, 2004 , Gong & Midkiff, 2005 ), but these multi-channel protocols require changes to existing standards and therefore cannot be deployed by using existing hardware. In ( Adya et al., 2004 ) was proposed a link-layer solution for transmitting data over multiple radio interfaces, but this approach is designed for scenario where the number of radio interfaces is equal to the number of channels. In ( Gupta & Kumar, 2000 ) the performance of multi-channel ad-hoc networks was studied, where each channel was assigned to an interface. In ( Draves et al., 2004 ) several methods for increasing the performance in single-channel per interface were proposed. The most studies is focused only to one problem - to find the efficient channel assignment method, but did not suggest the optimal number of radio interfaces per node. In ( Husnain et al., 2004 ) were compared different static centralized algorithms, but for evaluation of optimal number of radio interfaces was used only one parameter - total interference (number of links in conflict graph). ( Raniwala et al., 2004 ) proposed centralized channel assignment and routing method, where results about number of radio interfaces were shown but only for network cross-section goodput. In (C hi Moon Oh et al., 2008 ) the study of optimal number of radio interfaces was created but only for grid network, using simple channel assignment method and for one QoS parameter (throughput).

3. The study of optimal number of radio interfaces

In this section several simulations were created to find the optimal number of radio interfaces for static WMN. In this study we focus only to one problem - to find the optimal number of radio interfaces for different conditions therefore, for channel assignment we used simple CCA approach (section 5.1).

Nowadays the availability of the cheap off-the-shelf commodity hardware also makes multi-radio solutions economically attractive. This condition provides the using much more radio interfaces per node, which shows the investigating of optimal number of interfaces as a reasonable argument.

We have included in our simulations several QoS parameters, data flows, number of nodes and network topologies to find the optimum number of radio interfaces for services which required the real time transmission (e.g. video conference).

3.1. Simulation environment

A simulation WMN model was developed in NS-2 network simulator, with additional function to support multi-channel and multi-interface solution ( Calvo & Campo, 2007 ). Each mesh node used the number of interfaces between 1 to 8 and the same number of channels. Two different network topologies were created. The first one was grid topology, which consisted of 25 static wireless mesh nodes placed in an area of 1000 x 1000 meters. Transmission range for each node was set to 200 meters ( Fig.4a) . The second topology consists of 25 nodes, which were randomly placed in an area of 1000 x 1000 meters ( Fig.4b) . For simulation evaluations, ten random topologies and computed average values of chosen QoS parameters were studied. We have used the WMN with 25 nodes, because of the typical number of mesh nodes in WMN (25 to 30) ( Skalli et al., 2006 ). For traffic generation, 5 CBR (Constant Bit Rate) flows were used and the packet size was set to 512 bytes. The same radio default parameters as in ( ns-2, 2008 ) were used, except that we set the channel data rate to 11 Mbit/s. Simulation parameters are summarized in Table 1 .

Simulation parameters

Grid (a) and random (b) topology of static WMN created in NS-2 simulator

3.2. Simulation results

In this section results of experiments are presented. The purpose of simulation was to determine the optimal number of radio interfaces for different WMN topologies, different number of data flows and different number of nodes to achieve the network capacity increasing expressed in enhancement of QoS parameters.

We chose four QoS parameters for simulation evaluation:

Average End-to-end Delay : The average time taken for a packet to reach the destination. It includes all possible delays in the source node and in each intermediate host, caused by queuing at the interface queue, transmission at the MAC layer, routing discovery, etc. Only successfully delivered packets are counted.

Average Throughput : The sum of data packets delivered to all nodes in the network in a given time unit (second).

Packet Loss : Occurs when one or more packets being transmitted across the network fail to arrive at the destination.

Average Jitter : The delay variations between all received data packets.

3.2.1. Different network topologies

In this simulation we created two different network topologies of WMN (grid topology and random topology). Ten random topologies were created and average values of chosen QoS parameters were computed.

Average values of end-to-end delays for various radio interfaces and different network topologies

Figure 5 shows the average values of end-to-end delay for various numbers of radio interfaces and two different network topologies. From results it is obvious that the highest value of end-to-end delay (0.92 sec) was reached in the grid WMN with one radio interface. The lowest value of delay (0.0097 sec) was achieved in grid WMN with seven radio interfaces. In WMN with random topology, the lowest value of delay (0.049 sec) was achieved in WMN with six radio interfaces. The best values of average delay were achieved in WMN with random topology, but differences between values of random and grid topologies were small for higher number of radio interfaces. From results it may be concluded that optimal number of radio interfaces which guarantee the maximum allowable average delay 150 ms ( ITU-T, 2003 ) for both network topologies is five, because more than five interfaces improved value of end-to-end delay only slightly, but the complexity of node is increased considerably.

Figure 6 shows the average values of network throughput for various numbers of radio interfaces and two different network topologies. The lowest value of average throughput was achieved in grid WMN, where nodes have used for transmission only one radio interface. In this case, the value of average throughput was 504.28 kbps. In the case where WMN with random topology and one radio interface was used, the lowest value of average throughput (739.3 kbps) was achieved. The highest value of throughput (2019.9 kbps) reaches the grid WMN with seven radio interfaces. The best value of average throughput in random WMN topology (1964.2 kbps) was achieved by WMN with seven radio interfaces. Again, the optimal number of interfaces for both network topologies was chosen as five.

Average values of throughput for various radio interfaces and different network topologies

As we can see from Fig.7 , the highest value of packet loss (75.1%) was reached in grid WMN with one radio interface. The lowest value of packet loss was achieved in WMN with seven radio interfaces. This value was 3.5% for the random topology and 2.5% for grid topology. As in the previous case, we can conclude the optimal number of radio interfaces as five, where grid topology achieved 9.8% of packet loss and 7.6% for random topology.

Figure 8 shows the average values of time jitter for different types of topologies and various number of radio interfaces. From results it is obvious that the highest value of average jitter was reached in the network with one radio interface. For the random topology this value was 0.7 sec and for grid topology it was 0.8 sec. On the other hand the lowest values of average jitter were achieved in grid WMN with seven interfaces (0.3 sec) and in random WMN with six interfaces (0.05 sec). As an optimal number of radio interfaces, the number of six was selected with average jitter value 0.11 sec for random topology and 0.14 sec for grid topology.

Values of packet loss for various radio interfaces and different network topologies

Average values of jitter for various radio interfaces and different network topologies

3.2.2. Different number of data flows

Simulation model consisted of 25 static wireless mesh nodes placed in grid in area 1000x1000 m ( Fig.4a) . Transmission range for each node was set to 200 m. As traffic transmission, the 5, 10, 15 and 20 CBR flows were simulated and packet size of 512 bytes was used. Data flows were created between random chosen node pairs.

Figure 9 shows the average values of end-to-end delay for different number of data flows. From results it is obvious that the best performance was achieved in multi-interface WMN with six interfaces, when the number of flows changed. The highest value of average end-to-end delay (for all data flows) was reached by WMN with one radio interface. For small number of data flows (5), WMN with 5 interfaces reached the best performance, whilst for 10 data flows the best performance was reached by 6 interfaces. For more data flows (15 and 20) the system performance is unsatisfactory regardless of number of interfaces.

Average values of end-to-end delay for various radio interfaces and different number of data flows

Figure 10 shows the simulation results of average values of network throughput for the 5, 10, 15 and 20 data flows. The lowest value of average throughput was achieved in grid WMN with only one radio interface. From results it is obvious that the highest value of average throughput was reached in the multi-interface WMN with six radio interfaces. In the WMN with more than six interfaces the network performance is decreasing.

Average values of throughput for various radio interfaces and different number of data flows

As we can see from Figure 11 , the best value of packet loss was reached in multi-interface WMN with six radio interfaces. The highest value of packet loss was reached in WMN, where nodes used for transmission one radio interface.

Figure 12 shows the average values of jitter for the different number of data flows. The highest values were achieved in WMN, where nodes have used for transmission only one radio interface. The best value of average jitter for all data flows was achieved in WMN with five or six radio interfaces.

Values of packet loss for various radio interfaces and different number of data flows

Average values of jitter for various radio interfaces and different number of data flows

3.2.3. Different number of nodes

In this simulation the static grid WMN was used ( Fig. 4a), but with changing number of nodes. Six different N x N grid networks were created, where N was changed from five to ten. Transmission range for each node was set to 200 meters. For traffic transmission, 15 CBR flows were used and the packet size 512 bytes was set. Data flows were created between random chosen node pairs.

Results from previous sections (3.1.1 and 3.1.2) shows that the best values for almost all QoS parameters were achieved in WMN with six radio interfaces. For this reason the simulation model for different number of nodes only for WMN with six radio interfaces was created. Figure 13 shows the average values of end-to-end delay for six radio interfaces and six different network topologies. The best value of average end-to-end delay was reached in multi-interface WMN with 25 nodes (5x5). The highest value of average end-to-end delay was achieved by WMN with 100 nodes (10x10). Results show that increasing number of nodes increase value of end to end delay.

Average values of end-to-end delay for different number of nodes

The lowest value of average throughput ( Fig. 14) was achieved in WMN with 100 static nodes. The best values of throughput were reached in configuration 6x6 and 7x7 nodes.

Average values of network throughput for different number of nodes

The highest values of packet loss ( Fig. 15) were achieved in WMN with 10x10 nodes. The lowest value of packet loss was achieved in the WMN with 6x6 nodes.

Values of packet loss for different number of nodes

As we can see from figure 16 the best value of average jitter was achieved WMN with 25 nodes and the highest value was reached in 9x9 grid network.

Average values of jitter for different number of nodes

These simulations showed unacceptable values for almost all simulated QoS parameters. Average delay combined with average jitter achieved in all networks (from 25 to 100 nodes) doesn’t allow using several CBR services running simultaneously. This conclusion is certified by enormous packet loss in networks (over 55 % in the best solution).

3.3. Results summary

The results show the benefits of using multiple radio interfaces per node. This solution can improve the capacity of WMN. Simulation results show that by increasing the number of interfaces it is possible to increase network capacity by enhancing of QoS parameters. For all simulations of WMN with common channel assignment method, the number of six radio interfaces appears as an optimum solution, because further increasing of the number of interfaces improved the capacity of WMN only slightly and using more than seven radio interfaces decreased the network performance. These results can be used as a base to another research channel assignment methods, where using of suitable CA algorithm can additionally improve network performance.

4. Theoretical background

Optimal channel assignment in WMNs is an NP-hard problem (similar to the graph coloring problem). For this reason, before we present the channel assignment problem in WMNs, let us first provide some mathematical background about graph coloring problem.

4.1. Graph coloring

The graph coloring theory is used as a base for the theoretical modeling of channel assignment problem. At the beginning we must define two related terms: communication range and interference range . Communication range is the range in which a reliable communication between two nodes is possible. The interference range is the range in which transmission from one node can affect the transmission from other nodes on the same or partially overlapping channels. The interference range is always larger than the communication range ( Fig. 17) ( Prodan & Mirchandani, 2009 ).

Communication range and interference range

Consider an undirected graph G(V, E) that models the communication network. A graph G, is defined as a set of vertices V and a set of edges E. Each vertex in graph represents a mesh router and each edge between two vertices represents a wireless link between two mesh routers. The color of each vertex represents a non-overlapping channel and the goal of the channel assignment is to cover all vertices with the minimum number of colors such that no two adjacent vertices use the same channel (Husnain Mansoor Ali et al., 2009).

4.2. Connectivity graph

The vertices set V consists of the network nodes, which may have multiple radio interfaces (not necessarily the same), while the edges/links set E includes all the communication links in the network. A link e between a pair of nodes (v i , v j ) ; where v i , v j є V exists if they are within the communication range of each other and are using the same channel. The graph G described above is called the Connectivity graph ( Fig. 5) . The links presented in the network topology are referred to as the logical links ( Husnain Mansoor Ali et al., 2009 ).

4.3. Interfering edges

To include the interference in network model, we introduce the concept of Interfering edges. Interfering edges for an edge e (IE( e )) are defined as the set of all edges which are using the same channel as edge e but cannot use it simultaneously in active state together with edge e . All edges are competing for the same channel hence the goal of channel assignment algorithm is to minimize the number of all edges e thereby increasing capacity (Husnain Mansoor Ali et al., 2009).

4.4. Conflict graph

In this subsection the concept of conflict graph is introduced. A conflict graph G c ( V c , E c ) consist of the set of edges E c and the set of vertices V c . The vortices V c have a one relation with the set of edges E c of the connectivity graph (i.e. for each edge e ∈ E c , there exists a v c ∈ V c ). As for the set E c of the conflict graph, there exists an edge between two conflict graph vertices v ci and v cj if and only if the corresponding edges e i and e j of the connectivity graph, are in IE( e ) set of each other. Hence, if two edges interfere in the connectivity graph, then there is an edge between them in the conflict graph. The conflict graph can now be used to represent any interference model. For instance, we can say that two edges interfere if they use the same wireless channel and they are within interference range. If we want use any other interference model based on signal power, then that can also be easily created by just defining the conditions of interference. Total interference can now be described as the number of links in the conflict graph (i.e. the cardinality of E c ).

The above mentioned concepts of connectivity graph, interfering edges and conflict graph are illustrated in Fig.18 . For a graph G ( V, E ), we find the IE for all the links and then create the conflict graph G c ( V c , E c ) ( Husnain Mansoor Ali et al., 2009 ).

Connectivity graph, interfering edges and conflict graph

5. Channel assignment algorithms for WMN

As has been already mentioned, CA in a multi-interface WMN consists of assigning channels to the radio interfaces in order to achieve efficient channel utilization for minimizing interference and to guarantee an adequate level of connectivity. Nowadays, there exist many approaches to solve the channel assignment problem. These approaches can be divided into three main categories ( Conti et al., 2007 ):

Fixed (static) channel assignment approaches – channels are statically assigned to different radio interfaces. The main concern includes the enhancement of efficiency and guaranteeing of the network connectivity.

Dynamic channel assignment approaches – a radio interfaces are allowed to operate on multiple channels, implying that a radio interfaces can be switched from one channel to another one. This switching depends on channel conditions, such as the value of interference. The basic issues are the switching delay and the switching synchronization.

Hybrid channel assignment approaches – in this approach the radio interfaces are divided into two groups, the first is fixed for certain channels and the second is switchable dynamically while deploying the channels.

In this section several channel assignment approaches are compared by QoS parameters mentioned in the previous section.

5.1. Common channel assignment

The Common channel assignment (CCA) is a simplest fixed channel assignment approach ( Adya et al., 2004 ). In this CA approach all radio interfaces of each node were tuned to the same set of channels. For example, if every node has two radio interfaces then each node uses the same two channels ( Fig. 19) . The main benefit of this approach is the network connectivity. The connectivity is the same as that of a single interface approach, while the using of multiple radio interfaces can improve network throughput. However, if the number of non-overlapping channel is much higher than the number of radio interfaces, the gain of the CCA may be limited. CCA scheme presents a simplest channel assignment approach but it fails to account for the various factors affecting CA in a WMN. This solution will decrease the utilization of network resources ( Yulong Chen et al., 2010 ).

Example of common channel assignment approach

5.2. Load aware channel assignment

Load aware channel assignment (LACA) represents a dynamic centralized channel assignment and routing algorithm, where traffic is mainly directed toward gateway nodes ( Raniwala et al., 2004 ), assuming that the offered traffic load on each virtual link is known. Algorithm assigns channels by such a way to ensure the network connectivity while takes into account the bandwidth limitation of each link. At the beginning, LACA estimates the total expected load on each virtual link based on the load imposed by each traffic flow. In the next step CA algorithm visits each virtual link in decreasing order of expected traffic load and greedily assigns it a channel. The algorithm starts with an initial estimation of the expected traffic load and iterates over channel assignment and routing until the bandwidth allocated on each virtual link matches its expected load. While this CA approach presents a method for CA that incorporates connectivity and flow patterns, the CA scheme on links may cause a “ ripple effect ”, whereby already assigned links have to be revisited, thus increasing the time complexity of the scheme.

An example of node revisiting is illustrated in Fig. 20 . In this example each node has two radio interfaces. The channel list of node A is [1, 6] and channel list of node B is [2, 7]. Because nodes A and B have no common channel, a channel re-assignment is required. Link between nodes A and B needs to be assigned one of the channels from [1, 2, 6, 7]. Based on the channel expected loads, link between nodes A and B is assigned channel 6, and channel 7 assigned already to link between nodes B and D is reassigned to channel 6 ( Raniwala et al., 2004 , Yulong Chen et al., 2010 ).

An example of channel revisit in LACA approach

5.3. First random channel assignment

The First Random Channel Assignment algorithm (FRCA) is a dynamic and centralized load aware channel assignment and routing algorithm for multi-interface multi-channel WMN ( Pollak, Wieser, 2012 ). This approach takes into account the network traffic profile. FRCA algorithm assigns radio channels to links considering their expected loads and interference effect of other links, which are in interference range and which are tuned to the same radio channel.

FRCA algorithm consists of two basic phases:

Initial phase

Optimization phase

In the first phase, algorithm estimates initial loads on all links based on the initial routes created by routing algorithm. After load estimation, FRCA randomly assigns channels to all nodes for each radio interface.

In the second phase, FRCA algorithm uses similar steps as in the first phase, but channel assignment and routing iterations are based on results from the first phase. If some of the link load is higher than link capacity, the algorithm goes back and tries to find better solution. Algorithm’s iterations end when no further improvement is possible. In optimization phase, FRCA uses greedy load-aware channel assignment algorithm similar to the one used in LACA algorithm ( Raniwala et al., 2004 ). In this algorithm virtual links are visited in decreasing order of the link expected load. To find routes between nodes, FRCA uses shortest path routing based on minimum hop count metric ( Kaabi et al., 2010 ).

5.3.1. Link load estimation

This approach is based on the concept of load criticality. The method assumes perfect load balancing across all acceptable paths between each communicating pair of nodes. Let P ( s, d ) denote the number of acceptable paths between pair of nodes ( s, d ), P l ( s, d ) is the number of acceptable paths between ( s, d ) which pass a link l . And finally, let B(s, d) be the estimated load between node pair ( s, d ). Then the expected traffic load Φ l on link l is calculated as ( Raniwala et al., 2004 ):

This equation implies that the initial expected traffic on a link is the sum of the loads from all acceptable paths, across all possible node pairs, which pass through the link. Because of the assumption of uniform multi-path routing, the load that an acceptable path between a pair of nodes is expected to carry is equal to the expected load of the pair of nodes divided by the total number of acceptable paths between them. Let us consider the logical topology as shown in Fig. 21 and assume that we have three data flows reported in table 2 .

Multi-interface and multi-channel WMN

Because we have three different communications node pairs, we have

Traffic profile with three data flows

Possible data flows between communicating nodes

In the next step we calculate P ( s, d ) for each flow. We need to determine all the possible paths between source and destination. Table 3 shows all possible paths between communication node pairs for the WMN topology in Fig. 21 . Values P ( s, d ) and the corresponding link traffic load ( Φ l ) is calculated using equation (2) . Results are shown in table 4 . Based on these calculations, we can estimate the load between each neighboring nodes. The result of calculation Φ l is the expected traffic load of link l (i.e. the amount of traffic expected to be carried over a specific link) ( Badia et al., 2009 , Conti et al., 2007 , Raniwala et al., 2004 ).

The results of calculation Φ l on specific link l

5.3.2. Link capacity estimation

The link capacity (channel bandwidth available to a virtual link) is determined by the number of all virtual links in its interference range that are also assigned to the same radio channel. So when estimating the usable capacity of the virtual link, we should consider all traffic loads in its interference range. According to the channel assignment rules, the higher load a link is expected to carry, the more bandwidth it should get. On the other side, the higher loads its interfering links are expected to carry, the less bandwidth it could obtain. Thus, the link capacity should be proportional to its traffic load, and be inversely proportional to all other interfering loads. Thus, the capacity bw (i) assigned to link i can be obtained using the following equation:

where Φ i is the expected load on link i , Intf(i) is the set of all virtual links in the interference range of link i (i.e. links i and j operates on the same channel). C ch is the sustained radio channel capacity ( Badia et al., 2009 , Conti et al., 2007 , Raniwala et al., 2004 ).

5.4. Simulation results

In this section, the performance of proposed FRCA concept is evaluated and compared with CCA ( Adya et al., 2004 ), LACA ( Raniwala et al., 2004 ) and a single interface architecture by using NS-2 simulator (ns-2, 2008). Simulation model consisted of 25 static wireless mesh nodes placed in an area of 1000 x 1000 m ( Fig.4a) . The distance between nodes was set to 200 m. The capacity of all data links was fixed at 11Mbps. All nodes have the same transmission power and the same omni-directional antenna. The transmission range was set to 200 m and interference range was set to 400 m. For traffic generation, 25 CBR (Constant Bit Rate) flows with packet size 1000 bytes were used. Flows were created between randomly chosen node pairs. For simulation evaluation, the same metrics like in section 3.1 was used.

5.4.1. Different number of radio interfaces

From previous sections the conclusion about optimal number of six radio interfaces was gained. This conclusion was based on simple common channel assignment scheme CCA, which was used in simulations. With using more sophisticated channel assignment scheme it is possible to expect that the same results in QoS parameters may be reached with less number of interfaces. So the performance evaluation of chosen CA schemes was based on changing number of radio interfaces (between 2 to 8 radio interfaces for each node).

Figure 22 shows the average values of end-to-end delay for various number of radio interfaces. From results it is obvious that the highest value of delay (792.64 ms) was reached in WMN with CCA scheme. Lowest value (101.42 ms) reached WMN with FRCA algorithm for 4 radio interfaces. For CCA scheme the optimal number of radio interfaces was 6, but FRCA and LACA reached the best performance with only 4 radio interfaces. Results show that further increasing of number of radio interfaces didn’t increase the network performance, so the optimal number of radio interfaces for LACA and FRCA algorithm is 4.

Average values of end-to-end delay for various radio interfaces and different CA schemes

Figure 23 shows the average values of network throughput. The lowest value of average throughput for all radio interfaces was achieved in WMN with CCA scheme. This approach reached the best results for 6 radio interfaces. Others CA algorithms (FRCA and LACA) achieved the best performance with only 4 radio interfaces, with FRCA slightly outperformed LACA algorithm.

Average values of network throughput for various radio interfaces and different CA schemes

As we can see from figure 24 the highest value of packet loss for all number of interfaces was reached in WMN with CCA approach, with the best value reached for 6 radio interfaces (63.56 %). The best result (5.86 %) reached FRCA algorithm for 4 radio interfaces, whereas algorithm LACA with the same number of radio interfaces reached value 9.47%.

Figure 25 shows average values of average jitter. The best values of average jitter were again reached with FRCA algorithm for 4 radio interfaces (124.8 ms). CCA algorithm reached the best value for 6 radio interfaces (601.25 ms) and LACA approach for 4 radio interfaces (167. 27 ms).

Values of packet loss for various radio interfaces and different CA schemes

Average values of jitter for various radio interfaces and different CA schemes

6. Conclusion

In this chapter, the study of optimal number of radio interfaces and new channel assignment approach was presented (FRCA). The study of optimal number of radio interfaces was created for two different topologies (grid and random), different number of data flows and different number of nodes. The study was based on increasing number of radio interfaces (1 to 8) for each mesh nodes. The results show that by increasing the number of interfaces it is possible to increase network capacity by enhancing of QoS parameters. For all simulations of WMN with common channel assignment method CCA, the number of six radio interfaces appears as an optimum solution, because the further increasing of the number of interfaces improved the capacity of WMN only slightly and using more than seven radio interfaces decreased the network performance.

For further increasing of network performances more sophisticated channel assignment algorithms were used. The new channel assignment approach called First random channel assignment (FRCA) was compared with existing channel assignment algorithms (CCA, LACA). The results show that by using the suitable CA algorithm it is possible to further increase the network capacity. From all results it can be concluded that the multi interface approach with suitable CA algorithm can dramatically increase the whole network performance. In that case, if it is used the simplest CA approach (CCA), we need to assign for each node up to 6 radio interfaces to maximize network performance, but by using suitable dynamic CA algorithm (e.g. FRCA or LACA), the network performance may be maximized with only 4 radio interfaces.

Acknowledgement

This work was supported by the Slovak Scientific Grant Agency VEGA in the project No. 1/0704/12.

• 4. Chi Moon Oh; Hwa Jong Kim; Goo Yeon Lee & Choong Kyo Jeong 2008 A Study on the Optimal Number of Interfaces in Wireless Mesh Network, In International Journal of Future Generation Communication and Networking, IJFGCN 1 1 59 66 Dec. 2008
• 9. Husnain Mansoor Ali; Anthony Busson & Véronique Vèque 2009 Channel assignment algorithms: a comparison of graph based heuristics, In: Proceedings of the 4th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks, 120 127 October 26-26, 2009, Tenerife, Canary Islands, Spain
• 10. ITU-T 2003 ITU-T Recommendation G.114, 2003.
• 13. ns-2 2008 The Network Simulator ns-2, http://www.isi.edu/nsnam/ns/
• 20. Wei Yahuan; Taoshen Li & Zhihui Ge 2011 A Channel Assignment Algorithm for Wireless Mesh Networks Using the Maximum Flow Approach. In: Journal of networks, 6 6 June 2011
• 21. Yulong Chen; Ning Xie; Gongbin Qian & Hui Wang 2010 Channel assignment schemes in Wireless Mesh Networks, In: Mobile Congress (GMC), 2010 Global, vol., no., 1 5 Oct. 2010

© 2012 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Difference between Fixed and Dynamic Channel Allocations

Prerequisite – Channel Allocation Problem in Computer Network Fixed Channel Allocation (FCA): Fixed Channel Allocation is a strategy in which fixed number of channels or voice channels are allocated to the cells. Once the channels are allocated to the specific cells then they cannot be changed. In FCA channels are allocated in a manner that maximize Frequency reuse. If all channels are occupied and user make a call then the call is blocked. Borrowing Channels handles this type of problem. In this cell borrow channels from other cells. Dynamic Channel Allocation (DCA): Dynamic Channel Allocation is a strategy in which channels are not permanently allocated to the cells. When a User makes a call request then Base Station(BS) send that request to the Mobile Station Center(MSC) for the allocation of channels or voice channels. This way the likelihood of blocking calls is reduced. As traffic increases more channels are assigned and vice-versa. Difference between Fixed Channel Allocation(FCA) and Dynamic Channel Allocation(DCA):

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What is channel allocation in computer network?

When there are more than one user who desire to access a shared network channel, an algorithm is deployed for channel allocation among the competing users. The network channel may be a single cable or optical fiber connecting multiple nodes, or a portion of the wireless spectrum. Channel allocation algorithms allocate the wired channels and bandwidths to the users, who may be base stations, access points or terminal equipment.

Channel Allocation Schemes

Channel Allocation may be done using two schemes −

Static Channel Allocation

Dynamic channel allocation.

In static channel allocation scheme, a fixed portion of the frequency channel is allotted to each user. For N competing users, the bandwidth is divided into N channels using frequency division multiplexing (FDM), and each portion is assigned to one user.

This scheme is also referred as fixed channel allocation or fixed channel assignment.

In this allocation scheme, there is no interference between the users since each user is assigned a fixed channel. However, it is not suitable in case of a large number of users with variable bandwidth requirements.

In dynamic channel allocation scheme, frequency bands are not permanently assigned to the users. Instead channels are allotted to users dynamically as needed, from a central pool. The allocation is done considering a number of parameters so that transmission interference is minimized.

This allocation scheme optimises bandwidth usage and results is faster transmissions.

Dynamic channel allocation is further divided into centralised and distributed allocation.

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• What is Star Network Topology in Computer Network?
• What is Ring Network Topology in Computer Network?
• What is Tree Network Topology in Computer Network?
• What is Bus Network Topology in Computer Network?
• What is MultiDrop Network Topology in Computer Network?
• Assumptions for Dynamic Channel Allocation
• What is Payload in Computer Network?
• What is multicasting in Computer Network?
• What is Broadcasting in Computer Network?

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Channel Assignment Techniques

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• Gordon L. Stüber 2  

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First generation macrocellular systems typically use fixed channel assignment (FCA), where disjoint subsets of the available channels are permanently allocated to the cells in advance according to their estimated traffic loads. The cells are arranged in tessellating reuse clusters whose size is determined by the co-channel reuse constraint. For example, the North American AMPS system typically uses a 7-cell reuse cluster with 120° sectoring. The 12.5 MHz bandwidth allocation for AMPS can support a total of 416 two-way channels, 21 of which are control channels (one for each sector in a cluster), leaving a total of 395 traffic channels. This yields an allocation of 56 channels/cell with uniform FCA.

• Mobile Station
• Near Neighbor
• Channel Assignment
• Handoff Call
• Idle Channel

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Gordon L. Stüber

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© 1996 Springer Science+Business Media New York

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Stüber, G.L. (1996). Channel Assignment Techniques. In: Principles of Mobile Communication. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6268-6_11

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