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Vol. 164, Issue 2, February 2014, pp. 170-175

 

Bullet

 

Multiagent Reinforcement Learning Dynamic Spectrum Access in Cognitive Radios
 

1 Wu Chun, 2 Yin Mingyong, 2 Ma Shaoliang, 1 Jiang Hong

1 School of National Defense Technology, Southwest University of Science and Technology, Mianyang 621000, Sichuan, China
2 Institute of Computer Application, China Academy of Engineering Physics, Mianyang 621900, Sichuan, China
1 Tel.: 86-816089890, fax: 86-816089890

1 E-mail: soldier_wu@163.com

 

Received: 28 November 2013 /Accepted: 28 January 2014 /Published: 28 February 2014

Digital Sensors and Sensor Sysstems

 

Abstract: A multiuser independent Q-learning method which does not need information interaction is proposed for multiuser dynamic spectrum accessing in cognitive radios. The method adopts self-learning paradigm, in which each CR user performs reinforcement learning only through observing individual performance reward without spending communication resource on information interaction with others. The reward is defined suitably to present channel quality and channel conflict status. The learning strategy of sufficient exploration, preference for good channel, and punishment for channel conflict is designed to implement multiuser dynamic spectrum accessing. In two users two channels scenario, a fast learning algorithm is proposed and the convergence to maximal whole reward is proved. The simulation results show that, with the proposed method, the CR system can obtain convergence of Nash equilibrium with large probability and achieve great performance of whole reward.

 

Keywords: Cognitive radios, Multiagent reinforcement learning, Q-learning, Dynamic spectrum access.

 

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