Distributed allocation of the sensing times for cooperative spectrum sensing

In cognitive radio systems, the sparse assigned frequency bands are opened to secondary users. We consider the sensing of the frequency spectrum for cognitive radios, based on energy detection. It has been shown that the sensing reliability can be improved by using several cooperating cognitive radios that exchange their individual sensing information to a coordinator node. The coordinator node combines the received information in order to make a decision about the primary network presence. In this paper, we propose a decentralized Q-learning algorithm to share the sensing time among the cognitive radios in a way that maximizes the throughputs of the radios. Numerical results show the convergence of the proposed algorithm and allow to discuss the exploration strategy, the frequency of execution of the algorithm and its computational complexity.

[1]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[2]  K. J. Ray Liu,et al.  Evolutionary Game Framework for Behavior Dynamics in Cooperative Spectrum Sensing , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[3]  Geoffrey Ye Li,et al.  Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[4]  Elza Erkip,et al.  User cooperation diversity. Part I. System description , 2003, IEEE Trans. Commun..

[5]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[6]  Youyun Xu,et al.  A Q-Learning based sensing task selection scheme for cognitive radio networks , 2009, 2009 International Conference on Wireless Communications & Signal Processing.

[7]  Ana Galindo-Serrano,et al.  Distributed Q-Learning for Aggregated Interference Control in Cognitive Radio Networks , 2010, IEEE Transactions on Vehicular Technology.

[8]  Ian Millington,et al.  Artificial Intelligence for Games , 2006, The Morgan Kaufmann series in interactive 3D technology.

[9]  Georgios B. Giannakis,et al.  Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity , 2010, IEEE Transactions on Signal Processing.

[10]  Friedrich Jondral,et al.  Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency , 2004, IEEE Communications Magazine.

[11]  Shibiao He,et al.  A Distributed Cooperation Spectrum Sensing Scheme in Cognitive Radio Network , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[12]  S. M. Mishra,et al.  Spectrum Sensing : Fundamental Limits , 2009 .

[13]  Sergio Barbarossa,et al.  Cooperative diversity through virtual arrays in multihop networks , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[14]  H. Urkowitz.  Energy detection of unknown deterministic signals , 1967 .

[15]  Arumugam Nallanathan,et al.  Sensing Time and Power Allocation Optimization in Wideband Cognitive Radio Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.