Channel Assignment and Power Control Based on Stochastic Learning Game in Cognitive Radio Networks
WANG Zhi-yong1,2, ZHANG Hu-yin1, XU Ning1, HAO Sheng1
1. School of Computer, Wuhan University, Wuhan, Hubei 430072, China;
2. College of Computer Science and Technology, Hubei University of Science and Technology, Xianning, Hubei 437100, China
Abstract：Traditional cognitive radio spectrum allocation algorithms tend to ignore the influence of transmission power on network interference and have the drawback of high interaction cost between nodes.In response to these problems,by quantifying transmission power levels,we formulate the channel assignment and power control problem as a distributed non-cooperative game,in which each second user's purpose is to maximize the elastic traffic rewards.Formally,the formulated game is proved to be an exact potential game and converges to Nash equilibrium (NE) point.Furthermore,introducing the stochastic learning theory into game model,we propose a strategy selection algorithm based on stochastic learning,then the sufficient condition and strict proof for the convergence of this algorithm to pure strategy NE point are given.Finally,Simulation results show that the proposed algorithm can achieve high system throughput and improve users' satisfaction with a small amount of interactions.
汪志勇, 张沪寅, 徐宁, 郝圣. 认知无线电网络中基于随机学习博弈的信道分配与功率控制[J]. 电子学报, 2018, 46(12): 2870-2877.
WANG Zhi-yong, ZHANG Hu-yin, XU Ning, HAO Sheng. Channel Assignment and Power Control Based on Stochastic Learning Game in Cognitive Radio Networks. Acta Electronica Sinica, 2018, 46(12): 2870-2877.
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2018, Vol. 46 
