COGNITIVE RADIO FOR NEXT-GENERATION WIRELESS NETWORKS: AN APPROACH TO OPPORTUNISTIC CHANNEL SELECTION IN IEEE BASED WIRELESS MESH

Transcription

1 ACCEPTED FROM O PEN C ALL COGNITIVE RADIO FOR NEXT-GENERATION WIRELESS NETWORKS: AN APPROACH TO OPPORTUNISTIC CHANNEL SELECTION IN IEEE BASED WIRELESS MESH DUSIT NIYATO, NANYANG TECHNOLOGICAL UNIVERSITY EKRAM HOSSAIN, UNIVERSITY OF MANITOBA The authors provide an overview of the different components to achieve adaptability in a cognitive radio transceiver and discuss the related approaches. 5 ABSTRACT Cognitive radio has emerged as a new design paradigm for next-generation wireless networks that aims to increase utilization of the scarce radio spectrum (both licensed and unlicensed). Learning and adaptation are two significant features of a cognitive radio transceiver. Intelligent algorithms are used to learn the surrounding environment, and the knowledge thus obtained is utilized by the transceiver to choose the frequency band (i.e., channel) of transmission as well as transmission parameters to achieve the best performance. In this article we first provide an overview of the different components to achieve adaptability in a cognitive radio transceiver and discuss the related approaches. A survey of the cognitive radio techniques used in the different wireless systems is then presented. To this end, a dynamic opportunistic channel selection scheme based on the cognitive radio concept is presented for an IEEE based wireless mesh network. INTRODUCTION Frequency spectrum is the scarcest resource for wireless communications and may become congested to accommodate diverse types of air interfaces in next-generation wireless networks. To meet the growing demands, the Federal Communications Commission (FCC) has expanded the use of unlicensed spectral band. However, since traditional wireless communications systems utilize the frequency bands statically allocated by the FCC, they lack adaptability. Also, statistics show that depending on the time and location, the utilization of both licensed and unlicensed frequency bands could be low. Therefore, efficient methods are required for spectrum sharing among different systems, services, and applications in a dynamic wireless access environment. The concept of software defined radio and cognitive radio was introduced to enhance the efficiency of frequency spectrum usage [1]. Software radio improves the capability of a wireless transceiver by using embedded software to enable the radio transceiver to operate in multiple frequency bands. The cognitive ratio is a special type of software defined radio which is able to intelligently adapt itself to the changing environment. Efficient learning and intelligent decision making algorithms are keys to the implementation of cognitive radio to achieve the desired system objectives. In this article we first provide an overview of the major components observation, learning, decision making and planning, and acting processes in a cognitive radio system. The different techniques proposed for cognitive radio design (e.g., estimation technique, game theory, decision theory, and pricing) are also discussed. Then a survey of related work in the literature on the different types of wireless communications systems is presented. To this end, we propose a cognitive radio approach for dynamic selection of frequency band (channel) in IEEE based wireless mesh networks. In such a network, multiple access points (APs)/mesh routers communicate with a wireless Internet gateway router/ap using different frequency bands (channels); also, each AP/mesh router uses a different channel to communicate with the mesh clients in the corresponding service area. We assume that a wireless mesh node is equipped with an intelligent software defined radio which is able to estimate medium access control (MAC) and physical layer parameters such as collision probability and signal strength. A fuzzy logic controller along with a learning algorithm is used to learn the traffic load condition and transmission quality in the available wireless channels. The knowledge /9/$ IEEE IEEE Wireless Communications February 29

2 gained from the learning algorithm is used by a wireless node/mesh client to decide on the frequency band of transmission in a distributed manner. COGNITIVE RADIO FOR NEXT-GENERATION WIRELESS NETWORKS MOTIVATIONS FOR USING COGNITIVE RADIO The concept of cognitive radio was introduced to improve the frequency spectrum utilization in wireless networks. It was observed that some frequency bands in the radio spectrum are largely unused, while some are heavily used. In particular, while a frequency band is assigned to a primary wireless system/service at a particular time and location, the same frequency band is unused by this wireless system/service in other times and locations. This results in spectrum holes (also called spectrum opportunities). Therefore, the spectrum utilization can be improved substantially by allowing secondary users to utilize these spectrum holes. The evolving heterogeneous wireless systems will accommodate diverse wireless access standards (e.g., cellular wireless, wireless local area network [WLAN], wireless personal area network [WPAN], and wireless metropolitan area network [WMAN]) for different applications and services using a limited frequency spectrum. In a heterogeneous wireless access environment a mobile user would be able to connect to different wireless networks through multiple interfaces simultaneously. This heterogeneous environment for wireless access will promote high-speed network connectivity with seamless mobility. In such a system an intelligent software defined radio at the user mobile can observe performance, availability, and reliability of each access network so that the best decision on network selection and transmission can be made. Again, cognitive radio agents embedded in the service providers infrastructure can be used to customize wireless services to mobile users based on their specific requirements. In military applications cognitive radio can improve the reliability of communication, especially in the combat zone with high interference and vulnerability due to jamming. Cognitive radio would be also useful for telemedicine and emergency healthcare services with very stringent communications requirements. For example, transmission of voice, video, and still images from an accident site (or ambulance) to the hospital can help the physicians and nurses to provide emergency medicare services to injured patients. There are two major approaches in cognitive radio: dynamic spectrum allocation and opportunistic spectrum access. For dynamic spectrum allocation, information on spectrum occupation is used for channel allocation and planning on a long-term basis. On the other hand, with opportunistic spectrum access, instantaneous information of channel usage by a primary user is observed and utilized to grant access to secondary users to increase utilization on a shortterm basis. In general, opportunistic spectrum access provides more agility, but at the expense of higher computational complexity. BASIC COMPONENTS OF COGNITIVE RADIO In order to increase frequency spectrum utilization, the basic components/processes used to achieve adaptability of wireless transmission in cognitive radio are described below. Observation Process The observation process typically consists of measurement and noise reduction mechanisms. It can be either passive or active. In a passive observation approach the radio transceiver silently listens to the environment, while in an active observation approach special messages or signals are transmitted and measured to obtain information about the surrounding environment. A cognitive radio transceiver could observe the interference temperature [1] at the physical layer as well as other performance measures/parameters (e.g., collision probability, traffic load) in the MAC and network layers. Learning Process This refers to the process of extracting useful information from collected data. A learning process utilizes data from the observation process, and previous decisions and actions. For example, the transmitter can learn the network behavior (e.g., through interference or collision in physical and MAC layers, respectively) to gain knowledge of the operating environment. There are two types of learning algorithms, supervised and unsupervised. In a supervised learning algorithm, a large set of training examples with known solutions are used to train the algorithm. On the other hand, the objective of unsupervised learning is to cluster and categorize the input based on similarity. Another type of learning algorithm is reinforcement learning (i.e., learning through interactions), which is used when the correct solution is unknown. A reinforcement learning algorithm tries different actions and observes the outcomes (i.e., exploration). The information thus obtained is then used to select the best action in the future (i.e., exploitation). While supervised learning is suitable for offline operation (where sample data is available), reinforcement learning can be used in an online manner in which the system can learn and adapt in real time. Planning and Decision Making Process This refers to the process of using knowledge obtained from learning to schedule and prepare for the next transmission. If multiple choices of actions are available, a transceiver must decide to choose the best strategy to achieve the target objective. For example, when a cognitive radio transmitter schedules a transmission based on the frequency usage of a primary user, a decision on the transmission power needs to be made to achieve the acceptable level of interference caused to the primary receiver. Action This refers to the process of responding to the environment. The action of a transceiver is controlled by the planning and decision making process. In military applications, cognitive radio can improve the reliability of communication, especially, in the combat zone with high interference and vulnerability due to jamming. Cognitive radio would also be useful for telemedicine and emergency healthcare services. IEEE Wireless Communications February 29 47

3 Instead of using complicated mathematical formulation, fuzzy logic uses a humanunderstandable fuzzy set of membership functions and inference rules to obtain the solution that satisfies the desired objectives. APPROACHES IN COGNITIVE RADIO Different techniques and methods are required in a cognitive radio transceiver to realize the above processes. For observation, estimation techniques are required to remove noise from measured data. For learning, intelligent and proactive learning algorithms can be used. The optimization method and decision theory are used for planning and decision making. Estimation Technique Parameter estimation is important for the observation process in a cognitive radio transceiver to obtain information about the ambient network environment. Sophisticated sensing mechanisms are generally required to obtain multiple parameters (e.g., channel state, traffic load, neighborhood information) simultaneously. Also, adaptive and intelligent filtering techniques can be used to gain actual information about the environment. Game Theory Game theory is a mathematical tool developed to understand competitive situations in which rational decision makers interact to achieve their objectives. The basic concept of game theory is the rationality with which the players of the game will choose their actions based on their interests. The solution of the game is given by the actions through which all the players are satisfied with their received payoffs (i.e., returns). In [2] a game-theoretic adaptive channel allocation scheme was proposed for cognitive radio networks. In particular, a game was formulated to capture the selfish and cooperative behaviors of players. Evolutionary Computation Evolutionary computation is a problem solving method based on evolution of biological life in the real world. This could be achieved by simulating evolution behavior of individual structures, which includes the selection and the reproduction processes. The most common technique in evolutionary computation is the genetic algorithm, and it has been applied to cognitive radio [3]. In particular, a multi-objective genetic algorithm was used to optimize the performances of different protocol layers of a communication system. An individual was defined as genes of a chromosome composed of transmission parameters (transmit power, frequency, pulse shape, symbol rate, and modulation). The objective (fitness) was defined in terms of network performances (e.g., bit error rate, data rate). Techniques such as simulated annealing and tabu search for global optimization can be also used for cognitive radio design. Fuzzy Logic Fuzzy logic provides a simple way to obtain the solution to a problem based on imprecise, noisy, and incomplete input information. Instead of using complicated mathematical formulation, fuzzy logic uses a human-understandable fuzzy set of membership functions and inference rules to obtain the solution that satisfies the desired objectives. In general, there are three major components in a fuzzy logic control system: fuzzifier, fuzzy logic processor, and defuzzifier. While the fuzzifier is used to map the actual inputs into the fuzzy set, the fuzzy logic processor implements an inference engine to obtain the solution based on predefined sets of rules. Then the defuzzifier is applied to transform the solution to the actual output. To capture dynamic system behavior, fuzzy logic rules and membership functions need to be adaptive to the changing environment so that the desired solution can be achieved. Fuzzy logic is combined with a learning algorithm (i.e., neuro-fuzzy) that is able to adapt to the changing environment of a cognitive radio system. Markov Decision Process Decision theory is required for cognitive radio to choose the best action intelligently in response to environmental stimuli. A partially observable Markov decision process (POMDP) was used for dynamic spectrum access in an ad hoc network [4]. An opportunistic spectrum access method was developed to allow secondary users to use the radio spectrum by using a decentralized cognitive MAC protocol. The action of the transmitter was defined as sensing and accessing the channel if available, and the reward was defined as the amount of transmitted data. Pricing Theory The pricing mechanism impacts resource allocation in wireless networks since service providers want to maximize revenue while users want to minimize cost for the target quality of service (QoS) performance. Pricing theory can be used for resource management in cognitive radio systems. In [5] a dynamic pricing, resource allocation, and billing method was proposed for cognitive radio users with multiple wireless interfaces. In this system pricing and allocation of radio resources were performed based on an auction mechanism. The system learns the users bidding strategies by a Bayes optimal classifier, and a multi-unit sealed bid auction is performed to obtain the optimal decision for the service providers and users. Theory of Social Science The method and theory developed for social science were used by cognitive radio [6] to provide agility for secondary users to access spectrum holes. The problem of spectrum agile radio was modeled by a society of independent decision makers in which the radio devices are aware of the society to which they belong. That is, the action of one device affects the interests of other devices. This model was applied for a contention-based mechanism (e.g., carrier sense multiple access with collision avoidance [CSMA/CA] protocol). A radio regulatory rule for controlling the minimum contention window was applied to avoid any monopoly in the network. Reinforcement Learning A reinforcement learning algorithm learns by interacting with the environment. In [7] a reinforcement learning algorithm, Q-learning, was used for dynamic channel assignment in cellular networks. While the amount of traffic in each cell varies, the proposed algorithm learns and adapts the number of channels assigned to each cell so that the call blocking probability can be minimized. 48 IEEE Wireless Communications February 29

4 COGNITIVE RADIO IN DIFFERENT WIRELESS SYSTEMS IEEE AND NETWORKS Since IEEE based WLANs (e.g., 82.11b) and IEEE based WMANs (e.g., 82.16a) may operate in the same unlicensed frequency band, efficient spectrum management and planning are required. In [8] a reactive cognitive radio approach was proposed for sharing the spectrum between these networks. This scheme consists of three components for dynamic frequency selection, power control, and time agility. For dynamic frequency selection, an IEEE 82.11b AP scans all the subchannels and chooses the one with the highest received signal strength indicator (RSSI). Power control is used by both IEEE 82.16a and 82.11b nodes to obtain the minimum transmission power that satisfies the link quality requirement. Time agility was proposed to reschedule packet transmission to avoid interference if the channel quality is poor. IEEE NETWORKS The IEEE standard is currently being defined for wireless regional area networks (WRANs) to provide broadband Internet connectivity in rural and remote areas. This standard will operate in the licensed VHF and UHF bands used for TV services since many TV channels in these frequency spectra are largely unused in many regions. The coverage area of an IEEE network is much bigger (e.g., up to 1 km) than that of an IEEE based WMAN. IEEE will be the first wireless communication standard adopting intelligent software defined radio. Flexibility and adaptability are major requirements for this standard in which the primary service (i.e., TV service) must not be disturbed by the IEEE devices. In the physical layer modulation and coding can be adjusted based on the channel quality of each user. Also, transmission power can be adapted to avoid interference to the primary service as well as to minimize self-interference among IEEE devices. IEEE devices would be able to measure channel quality, and select and schedule the use of frequency bands dynamically. Channel measurement is one of the most important mechanisms of the IEEE MAC, since an transmitter must detect and adapt channel usage to avoid interference with the primary service. ULTRA WIDEBAND-BASED WIRELESS PERSONAL AREA NETWORKS In ultra wideband (UWB) networks, data transmission takes place over a very large bandwidth using low transmission power density in order to achieve very high data rate over a short transmission range. Since the data signal is spread over a large bandwidth, UWB spectrum may overlap with that of other narrowband wireless systems. Therefore, adaptation of transmission is required to avoid interference. In [9] cognitive UWB radio based on adaptive pulse waveform generation was proposed. The cognitive pulse generator learns the distortion in generated pulse waveform and then uses pre-equalization to reduce this distortion. COOPERATIVE DIVERSITY WIRELESS NETWORKS Similar to multiple-input multiple-output (MIMO) wireless networks, in a cooperative diversity network the signal is transmitted and received over multiple antennas. However, in cooperative diversity these antennas are physically separated, and the signal is relayed by intermediate nodes to the destination node. Amplify-and-forward (AF) and decode-and-forward (DF) are two forwarding techniques in a cooperative diversity system. In [1] a cognitive radio for AF cooperative diversity wireless networks was presented. In this environment there are primary (licensed) and secondary (unlicensed) users sharing the same frequency spectrum, and a secondary user must detect the presence of a primary user as quickly as possible to avoid performance degradation. In this case, if there are several secondary users, cooperation among them could improve the agility of spectrum usage significantly. RESEARCH ISSUES IN PROTOCOL DESIGN FOR COGNITIVE RADIO NETWORKS Lightweight and Cooperative Protocols for Cognitive Radio Networks Since wireless devices are generally battery-limited, energy consumption for the execution of estimation, learning, and decision making algorithms should be minimized. Therefore, lightweight protocols would be required to implement cognitive radio networks. Again, cognitive radio components in each node can cooperate to improve the efficiency of frequency usage due to the fact that some nodes might be unable (e.g., due to their locations) to observe the environment accurately. With cooperative cognitive radio, the information obtained from observation and knowledge gained from a learning algorithm can be exchanged and shared among wireless nodes. Cross-Layer Optimization in Cognitive Radio Networks To optimize QoS performance in a cognitive radio network, parameter observation, learning, and decision making processes must be performed at the physical, MAC, network (routing), transport (congestion control), and application layers. For example, in the physical layer the interference temperature of the target frequency spectrum needs to be learned. In a multiuser environment the collision rate due to simultaneous channel access needs to be observed. When information is exchanged and a decision is made among layers, performance enhancement can be expected. AN APPLICATION OF COGNITIVE RADIO FOR DYNAMIC CHANNEL SELECTION IN IEEE BASED WIRELESS MESH NETWORKS In this section we present a dynamic channel selection scheme for opportunistic spectrum access based on cognitive radio for a wireless node in an IEEE based wireless mesh IEEE will be the first wireless communication standard adopting intelligent software defined radio. Flexibility and adaptability are major requirements for this standard in which the primary service (i.e., TV service) must not be disturbed by the IEEE devices. IEEE Wireless Communications February 29 49

5 Internet F1 A1 A2 F3 A3 Gateway router F4 A4 Mesh router F2 SYSTEM MODEL: IEEE MESH NETWORK We consider a two-hop mesh network (Fig. 1) that provides wireless Internet connectivity in a particular service area (e.g., a hotspot). Five IEEE mesh routers/aps 1 are deployed in a service area of size 1 m 1 m, where routers 1 to 4 are connected with the gateway router (i.e., router 5). The radio transceiver at each mesh router uses two frequency bands/channels, one for connecting to the mesh clients/wireless nodes and the other for connecting to the gateway router. To avoid interference, different mesh routers use different frequency bands (Fig. 1) to communicate with the mesh clients and the gateway router. Since there is no centralized controller, each wireless node has to select the transmission channel in a distributed manner. We consider the IEEE based distributed coordination function (DCF) MAC scheme with four-way handshaking (request-tosend, clear-to-send, data transmission, and acknowledgment) in each node. Service area Wireless node A1 A4: Channels used by mesh router and gateway router F1 F4: Channels used by wireless node and mesh router Figure 1. Locations of IEEE mesh routers in a service area (hotspot). 1 IEEE 82.11b or 82.11g-based off-theshelf radio equipment can be used for this. network. Architectures and protocols for routing and channel assignment in IEEE based multichannel wireless mesh networks have been studied previously in the literature. In [11] a channel selection scheme for multichannel IEEE networks was presented. In [12] a link layer protocol, Slotted Seeded Channel Hopping (SSCH), which utilizes frequency diversity was proposed to improve the capacity of IEEE based WLANs. In [13] a MAC scheme, MultiNet, was proposed for IEEE networks, which improves a connection s performance by connecting to multiple networks simultaneously on a single wireless adapter. In [14] a distributed dynamic channel allocation algorithm (among APs) was implemented, and experimental performance evaluation results were obtained. Different from the above, in this article we present a cognitive-radio-based dynamic channel selection scheme for wireless nodes in an IEEE based wireless mesh hotspot. In the proposed scheme a wireless node/mesh client learns physical (i.e., signal strength) and MAC layer (i.e., collision probability) parameters, and accordingly selects the best channel to connect to a mesh router. The decision can be made independently in each node in a distributed manner by using an intelligent algorithm. With the proposed scheme, there is no need to modify the MAC protocol as in the aforementioned approaches. COGNITIVE-RADIO-BASED DYNAMIC OPPORTUNISTIC CHANNEL SELECTION SCHEME The components of the proposed dynamic channel selection scheme used in each wireless node are shown in Fig. 2. The first part is for measuring instantaneous collision probability and signal strength, both of which impact the connection throughput at each channel. A low-pass filter is used to remove the instantaneous fluctuations (i.e., noise) from this measurement data. The second part is used to estimate traffic load by using a fuzzy logic controller and a reinforcement learning algorithm. The learning algorithm is similar to that proposed for the market selection problem in [15]. As shown in Fig. 2, the fuzzy logic controller is composed of a fuzzification process and inference rules [15]. Since the estimated collision probability P ~ c(f) is often very imprecise and cannot be computed very accurately, the fuzzification process is used to convert the imprecise collision probability in each channel into a fuzzy se, which is then used by the inference rules to estimate the amount of traffic load in each channel. Also, the estimated signal strength γ f is used to compute the corresponding wireless node utility. The algorithm combines the knowledge about traffic load with the wireless node utility to determine the potential gain of choosing a particular channel. The results obtained by using the learning algorithm are then used by a node to select the transmission channel accordingly. Wireless Node Utility The decision on dynamic channel selection at each node is based on utility (i.e., benefit from the chosen action), which is a function of collision probability P c (f) and received signal strength γ f in channel f. Note that this received signal strength is a function of distance between the AP and the wireless node, and the channel condition. Both collision probability and received signal strength impact the throughput and error performances experienced by a wireless node. In this article the following utility function is considered: 5 IEEE Wireless Communications February 29

6 Observe Learn Decide and act Collision measurement Signal strength measurement P c (f) γ f U ( Pc ( f), γ f) = T( Pc( f)). γref We use fuzzy logic and a learning algorithm at each node to estimate the parameters of this utility function. Fuzzy Logic We use collision probability as an indicator of traffic load in each channel. In the proposed fuzzy logic controller, collision probability can be either high or low (i.e., in fuzzy set c x ). Then, based on the fuzzified collision probability, the inference rules are used to gain information on the traffic load condition in a channel. Let P ~ c(f) denote the estimated collision probability in channel f. Then the inference rules can be expressed as follows: Rule R k : IF (P ~ c(1) is c x ) AND AND (P ~ c(f) is c y ), THEN U i,f is U i,f,k, γ f Fuzzification Figure 2. Cognitive-radio-based dynamic channel selection scheme. m f,i where F is the total number of available channels, U i,f,k is the utility corresponding to rule k (i.e., U i,f,k = U (P c (f),γ f )), and U i,f is the resultant utility for node i using channel f. For example, if there are two channels and the first rule is written as follows: Rule R 1 : IF (P ~ c(1) is high ) AND IF (P ~ c(2) is high ) THEN U i,f is U i,f,k, Inference rule Rule 1 Rule 2... Rule k Compute utility in different load situation M k Learning algorithm U i,f,k U i,f Channel selection In this case the normalized fitness is given as follows: M M = k k. K = M l 1 l Learning Algorithm A learning algorithm is used to approximate the utility U i,f,k perceived by each wireless node corresponding to the different traffic load conditions in the service area. Inference rules of the fuzzy logic controller are used to update the utility by using estimated collision probability P ~ c(f) and received signal strength γ f at channel f as follows: new old Uifk,, = ( 1 αmk ) Ui, f, k + αmk U ( P c( f), γf), where U old i,f,k denotes the utility of the previous learning iteration and α is the learning rate. In this learning algorithm, the utility of a wireless node is computed based on the history and current information (i.e., U old i,f,k and U (P c (f),γ f ), respectively). Note that the value of U i,f,k above is given by U new i,f,k. Also, the fuzzy logic controller provides information on traffic load information to the learning algorithm (i.e., M k ). This learning algorithm is a special type of Q-learning, which is a form of reinforcement learning algorithm that does not require a model of the environment and can be designed to operate in an online manner. The algorithm combines the knowledge about traffic load with the wireless node utility to determine the potential gain of choosing a particular channel. The results obtained by using the learning algorithm are then used by a node to select the transmission channel accordingly. the inference rule reads as IF collision in channel one is high AND collision in channel two is high THEN the utility of node i for using channel f is U i,f,k. Let m f,i denote the membership function for channel f obtained from fuzzification. This m f,i can be obtained using a standard fuzzification method [15]. Then the fitness of rule k to the traffic load condition can be obtained from M k = Π F f=1 m f,i. The estimated utility can then be calculated as follows: Uif, = K Mk U k= 1 i, f, k. K M k= 1 k Decision on Channel Selection The decision on channel selection is made based on U i,f. In particular, wireless node i chooses channel f that provides the highest U i,f. This channel selection scheme is executed periodically. Note that the decision can be made if the estimated collision probability and received signal strength change by an amount larger than the predefined thresholds, which implies that one or more new nodes are accessing the channel and/or some nodes have terminated connections with the corresponding mesh router. PERFORMANCE EVALUATION Simulation Setup We consider an IEEE mesh network with the configuration shown in IEEE Wireless Communications February 29 51

7 AP Node 2 4 AP Node (a) (b) Figure 3. Wireless nodes and the associated mesh routers: a) at time ; b) after 3 minutes. Fig. 1. Each router operates in DCF mode. We assume that each of the mesh clients always has a packet to transmit. Four different data rates can be used at each wireless node depending on the signal strength. In particular, data rates of 11, 5.5, 2, and 1 Mb/s are used if the signal strength is in the interval [8, ), [6, 8), [4, 6), (, 6) db. The distribution of the wireless nodes/mesh clients in a service area is assumed to be normal about the center of the service area. As the variance of the distribution becomes higher, the locations of the nodes in the service area becomes more uniform. For the channel selection scheme we set α =.1, and it is executed at each node periodically every 2 min. A time-driven simulator based on MATLAB is used. Performance of the Channel Selection Scheme: Association among the Wireless Nodes and Mesh Routers With uniform wireless node distribution in the service area, Fig. 3a shows the associations among the wireless nodes and mesh routers when a node chooses the transmission channel in a random order. The result of the adaptation of the channel selection scheme is shown in Fig. 3b. Even though during the adaptation period a wireless node may not choose the mesh router with which it is currently associated (Fig. 3b), eventually the channel selection scheme will converge to make the node choose the closest mesh router (due to the uniformity of node distribution). Next, we change the distribution of wireless nodes so that the node density in the service area becomes nonuniform (e.g., node density is higher in some locations [Fig. 4a]). Different from the case of uniform distribution, a node located in a high density region may not associate with the closest mesh router since this may result in very high collision probability in the transmission channel from that node to the mesh router. For nonuniform node distribution, variation in average node throughput and the corresponding 95 percent confidence interval under different numbers of nodes in the service area are shown in Fig. 4b. As expected, the throughput of an individual node decreases as the total number of nodes increases due to higher congestion. In this case nodes associated with mesh router 5 experience the lowest throughput due to the highest node density around that router. Since the node density around mesh router 2 is lowest, the nodes in that region experience the highest throughput. Figure 5 shows the utility of all the nodes in the network under different values of the variance of the distribution of the locations of the nodes assuming a total of 1 nodes. As expected, the proposed channel selection algorithm performs much better than other schemes (i.e., best signal strength and random schemes). However, when the node distribution becomes more uniform (i.e., large variance around the center), the scheme based on the best signal strength performs as well as the proposed algorithm. Therefore, the proposed algorithm would be more suitable for service areas with nonuniform node density. For the convergence of the proposed channel selection strategy, a wireless node can learn and dynamically adapt the channel selection so that the total throughput is maximized. We observe that the rate of convergence depends on the learning rate α. In particular, for a small value of α, the convergence rate is low. However, large values of α result in fluctuations, and the algorithm does not converge. In such a case the learning algorithm relies too much on the current information and tries to adapt quickly. CONCLUSION Cognitive radio has emerged as a new paradigm for designing wireless network architectures and protocols to maximize frequency spectrum utilization. An overview of the different compo- 52 IEEE Wireless Communications February 29

8 AP Node AP 1 AP 2 AP 3 AP 4 AP Dense node area Node throughput (Mb/s) Total number of nodes (a) (b) Figure 4. a) Wireless nodes and the associated mesh routers (for nonuniform node distribution); b) variation in average node throughput. nents in cognitive radio and the related approaches have been presented. Applications of cognitive radio techniques in different wireless networks have been discussed, and the related work in the literature has been reviewed. Some research challenges in the design of cognitive radio network protocols have been outlined. To this end, we have presented a cognitive radio approach to dynamic channel selection for opportunistic spectrum access in IEEE based multichannel wireless mesh networks (e.g., WiFi hotspots). The proposed scheme integrates fuzzy-logicbased estimation with a learning algorithm used at a wireless node for dynamic channel selection based on traffic load condition (or congestion quality of each channel) so that the utility of the node can be maximized. The performance of the proposed scheme has been evaluated by simulations, and it has been observed that it performs significantly better than some of the traditional schemes, especially with nonuniform node distribution in the service area. ACKNOWLEDGMENT This work was supported by a scholarship from TRLabs, Winnipeg, Canada, and in part by a grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada. REFERENCES [1] S. Haykin, Cognitive Radio: Brain-Empowered Wireless Communications, IEEE JSAC, vol. 23, no. 2, Feb. 25, pp [2] N. Nie and C. Comaniciu, Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks, Proc. IEEE DySPAN 5, Nov. 25, pp [3] T. W. Rondeau et al., Cognitive Radios with Genetic Algorithms: Intelligent Control of Software Defined Radios, Proc. SDR Forum Tech. Conf., 24, pp. C-3 C-8. [4] Q. Zhao, L. Tong, and A. Swami, Decentralized Cognitive MAC for Dynamic Spectrum Access, Proc. IEEE DySPAN 5, Nov. 25, pp [5] C. Kloeck, H. Jaekel, and F. K. Jondral, Dynamic and Local Combined Pricing, Allocation and Billing System with Cognitive Radios, Proc. IEEE DySPAN 5, Nov. 25, pp Utility x Figure 5. Effect of uniformity of node distribution on the network utility. [6] S. Mangold, S. Shankar, and L. Berlemann, Spectrum Agile Radio: A Society of Machines with Value-Orientation, Proc. Euro. Wireless Conf., vol. 2, 25, pp [7] J. Nie and S. Haykin, A Q-Learning-based Dynamic Channel Assignment Technique for Mobile Communication Systems, IEEE Trans. Vehic. Tech., vol. 48, no. 5, Sept. 1999, pp [8] X. Jing et al., Reactive Cognitive Radio Algorithms for Co-Existence Between IEEE 82.11b and 82.16a Networks, Proc. GLOBECOM 5, vol. 5, Nov. Dec. 25, pp [9] X. Zhou et al., Cognospectrum: Spectrum Adaptation and Evolution in Cognitive Ultra-Wideband Radio, Proc. IEEE ICU 5, Sept. 25, pp [1] G. Ganesan and Y. Li, Agility Improvement through Cooperative Diversity in Cognitive Radio, Proc. IEEE DySPAN 5, vol. 5, Nov. Dec. 25, pp [11] A. Nasipuri and S. R. Das, Multichannel CSMA with Signal Power-Based Channel Selection for Multihop Learning Best signal strength Random Location variance IEEE Wireless Communications February 29 53

9 Wireless Networks, Proc. IEEE VTC 2, vol. 1, Sept. 2, pp [12] V. Bahl, R. Chandra, and J. Dunagan, SSCH: Slotted Seeded Channel Hopping for Capacity Improvement in IEEE Ad Hoc Wireless Networks, Proc. ACM Mobicom 4, Sept. Oct. 24. [13] R. Chandra and P. Bahl, MultiNet: Connecting to Multiple IEEE Networks Using a Single Wireless Card, Proc. IEEE INFOCOM 4, vol. 2, Mar. 24, pp [14] D. Malone et al., Experimental Implementation of Optimal WLAN Channel Selection without Communication, Proc. IEEE DySPAN 7, Apr. 27, pp [15] H. Ishibuchi, R. Sakamoto, and T Nakashima, Learning Fuzzy Rules from Iterative Execution of Games, Fuzzy Sets and Sys., 23. BIOGRAPHIES DUSIT NIYATO [S 5] (dniyato@ntu.edu.sg) is currently an assistant professor in the School of Computer Engineering at Nanyang Technological University, Singapore. He received his Bachelor s degree in computer engineering from King Mongkut s Institute of Thechnology Ladkrabang, Thailand, in He obtained his M.Sc. and Ph.D. from the Department of Electrical and Computer Engineering at the University of Manitoba in 25 and 28, respectively. From 1999 to 23 he worked as a software developer at Embedded Systems Labs, Thailand. His main research interests are in the areas of radio resource management and performance modeling for broadband wireless networks, and cognitive radio systems. EKRAM HOSSAIN [S 98, M 1, SM 6] (ekram@ee.umanitoba.ca) is currently an associate professor in the Department of Electrical and Computer Engineering at the University of Manitoba, Winnipeg, Canada. His current research interests include design, analysis, and optimization of wireless communication networks and cognitive radio systems. He is a co-author/co-editor of the books Dynamic Spectrum Access and Management in Cognitive Radio Networks (Cambridge University Press, 29), Heterogeneous Wireless Access Networks (Springer, 28), Introduction to Network Simulater NS2 (Springer, 28), Cognitive Wireless Communication Networks (Springer, 27), and Wireless Mesh Networks: Architectures and Protocols (Springer, 27). He serves as an Editor for IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, IEEE Wireless Communications, IEEE Communications Surveys and Tutorials, and several other international journals. He is a registered Professional Engineer in the Province of Manitoba, Canada. 54 IEEE Wireless Communications February 29