COGNITIVE RADIO FOR NEXT-GENERATION WIRELESS NETWORKS: AN APPROACH TO OPPORTUNISTIC CHANNEL SELECTION IN IEEE 802.11-BASED WIRELESS MESH Dusit Niyato, Nanyang Technological University Ekram Hossain, University of Manitoba IEEE Wireless Communication Feb. 2009

Outline Introduction Cognitive Radio Basic Components, Approaches In Different Wireless Systems Research Issues in Protocol Design An Approach to Opportunistic Channel Selection in IEEE 802.11-Based Wireless Mesh System Model Dynamic Opportunistic Channel Selection Scheme Performance Evaluation Conclusion Comments

Introduction Frequency spectrum is the scarcest resource for wireless communications may become congested to accommodate diverse types of air interfaces in next-generation wireless networks Software radio Improves the capability of a wireless transceiver by using embedded software Enable the radio transceiver to operate in multiple frequency bands Cognitive radio A special type of software defined radio Able to intelligently adapt itself to the changing environment

Cognitive Radio Basic Components Observation Process Learning Process Measurement and noise reduction mechanism Passive observation The radio transceiver silently listens to the environment. Active observation Special messages or signals are transmitted and measured to obtain information about the surrounding environment Learning Process Extract useful information from collected data Reinforcement learning algorithm is used when the correct solution is unknown Learning through interactions

Cognitive Radio (cont’d) Planning and Decision Making Process Using knowledge obtained from learning to schedule and prepare for the next transmission A transceiver must decide to choose the best strategy to achieve the target objective Action The action of a transceiver is controlled by the planning and decision making process

Cognitive Radio (cont’d) Approaches Estimation Technique Obtain information about the ambient network environment Game Theory Evolutionary Computation Genetic algorithm Fuzzy Logic Markov Decision Process Pricing Theory Theory of Social Science Reinforcement Learning

Cognitive Radio (cont’d) In different wireless systems IEEE 802.11 and 802.16 Networks May operate in the same unlicensed frequency band Efficient spectrum management and planning are required IEEE 802.22 Networks (WRANs) The first wireless communication standard adopting intelligent software defined radio Ultra Wideband-based (WPANs) Cooperative Diversity Wireless Networks Primary users and secondary users

Cognitive Radio (cont’d) Research issues in protocol design Lightweight and cooperative protocols for cognitive radio networks Battery-limited, energy consumption for the execution of estimation, learning, and decision making algorithm should be minimized Cross-layer optimization in cognitive radio networks To optimize QoS performance in a cognitive radio network

Dynamic Channel Selection Scheme In the proposed scheme A wireless node/mesh client learns physical (i.e., signal strength) and MAC layer (i.e., collision probability) 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

Dynamic Channel Selection Scheme (cont’d) System Model IEEE 802.11 Mesh 100m*100m No centralized controller

Dynamic Channel Selection Scheme (cont’d) Fuzzy logic controller Pc(f): collision probability on channel f  estimate the amount of traffic load γf: estimated signal strength

Dynamic Channel Selection Scheme (cont’d) Wireless node utility The decision on dynamic channel selection at each node is based on utility function of collision probability Pc(f) and received signal strength γf on channel f. Both collision probability and received signal strength impact the throughput and error performances experienced by a wireless node.

Dynamic Channel Selection Scheme (cont’d) Fuzzy logic Use “collision probability” as an indicator of traffic load in each channel The interference rules are used to gain information on the traffic load condition in a channel Estimated collision prob. Result utility Example:

Dynamic Channel Selection Scheme (cont’d) Let mf,i denote the membership function for channel f obtained from fuzzification. This mf,i can be obtained using a standard fuzzification method. Then the fitness of rule k to the traffic load condition can be obtained from The estimated utility The normalized fitness

Dynamic Channel Selection Scheme (cont’d) Learning algorithm is used to approximate the utility Ui,f,k perceived by each wireless node corresponding to the different traffic load condition in the service area α: the learning rate Uoldi,f,k: the utility of the previous learning iteration

Dynamic Channel Selection Scheme (cont’d) Decision on Channel Selection Wireless node i chooses channel that provides the highest Ui,f This channel selection is executed periodically. 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.

Dynamic Channel Selection Scheme (cont’d) Performance Evaluation Each router operates in DCF mode For the channel selection scheme we set α: 0.1, and it is executed at each node periodically every 2 min. Using MATLAB to run the time-driven simulation

Dynamic Channel Selection Scheme (cont’d) Wireless nodes and the associated mesh routers: a) at time 0; b) after 30 minutes

Dynamic Channel Selection Scheme (cont’d) Wireless nodes and the associated mesh routers (for non-uniform node distribution) Variation in average node throughput

Dynamic Channel Selection Scheme (cont’d) Effect of uniformity of node distribution on the network utility

Conclusion An overview of the difference components in cognitive radio and the related approaches have been presented The dynamic channel selection for opportunistic spectrum access in IEEE 802.11-based multichannel wireless mesh networks It performs significantly better than some of the traditional schemes, especially with non-uniform node distribution in the service area.

Comments Provide an introduction to cognitive radio’s approaches. Learning rate selection is a issue. Convergence and performance Comparison with other channel selection scheme?