Joint channel selection and cluster-based routing scheme based on reinforcement learning for cognitive radio networks

Yasir Saleem, * and Yau, Alvin Kok-Lim * and Hafizal Mohamad, and Nordin, Ramli and Rehmani, Mubashir Husain (2015) Joint channel selection and cluster-based routing scheme based on reinforcement learning for cognitive radio networks. In: IEEE 2015 International Conference on Computer, Communication and Control Technology (I4CT 2015), Kuching, 21-23 April 2015. IEEE, pp. 21-25.

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Cognitive radio network (CRN) has emerged as a promising solution to solve the problem of underutilization of licensed spectrum. It allows opportunistic access of unutilized spectrum (or white spaces) by unlicensed users (or secondary users, SUs) whilst minimizing interference to licensed users (or primary users, PUs). The dynamicity of channel availability has imposed additional challenges for routing in CRNs. Besides providing optimal routes to SUs for communication, one of the key requirements of routing in CRNs is to minimize interference to PUs. In this paper, we propose a joint channel selection and cluster-based routing scheme called SMART (SpectruM-Aware cluster-based RouTing) for CRNs. SMART enables SUs to form clusters in the network, and subsequently, it enables SU source node to search for a route to its destination node in the underlying clustered network. SMART applies an artificial intelligence approach called reinforcement learning in order to maximize network performance, such as SU-PU interference and packet delivery ratio. Simulation results show that SMART reduces significant interference to PUs without significance degradation of packet delivery ratio when compared to clustered scheme without cluster maintenance (i.e., SMART-NO-MNT) and non-clustered scheme (i.e., spectrum-aware AODV or SA-AODV).

Item Type: Book Section
Additional Information: First and second authors are with Department of Computer Science and Networked System,Sunway University; 3rd and 4th authors are with Wireless Network and Protocol Research Lab, MIMOS Berhad; 5th author is with Department of Electrical Engineering, COMSATS Institute of Information Technology
Uncontrolled Keywords: Cognitive radio networks; clustering; routing; reinforcement learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Sunway University > School of Engineering and Technology [formerly School of Science and Technology until 2020] > Dept. Computing and Information Systems
Depositing User: Ms. Molly Chuah
Related URLs:
Date Deposited: 27 Mar 2016 10:43
Last Modified: 12 Oct 2020 07:41

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