认知无线电网络中基于强化学习的智能信道选择算法  

Reinforcement Learning Based Intelligent Channel Selection Algorithm for Cognitive Radio Networks

作　　者：刘洋[1] 崔颖 李鸥[1] 

机构地区：[1]信息工程大学信息系统工程学院 [2]65017部队

出　　处：《信号处理》2014年第3期253-260,共8页Journal of Signal Processing

基　　金：国家高技术研究发展计划(2012AA121606)

摘　　要：认知无线电系统不仅要具有自适应性,更应具备一定的智能性。该文将强化学习理论引入到认知无线电系统中,用于解决次用户在频谱感知过程中的信道选择问题,提出了一种基于强化学习的信道选择算法。该算法在未知主用户占用规律和动态特性的前提下,仅通过不断与环境进行交互学习,便能够引导次用户选择"较好"信道优先进行感知,使次用户吞吐量得到提高。仿真结果表明,相对于现有信道选择算法,所提算法可有效提高次用户的吞吐量,并且在主用户使用规律发生变化时,能够自动实现二次收敛,可作为认知无线电系统迈向智能化的一种尝试。More than an adaptive system,the cognitive radio system is an intelligent system.The reinforcement learning of the intelligent control theory is adopted in this paper,to solve the channel selection problem among the secondary users in spectrum sensing.A reinforcement learning based channel selection algorithm is proposed to increase the throughput of the secondary users.The algorithm allocates selection probabilities to the secondary users through times of interaction with the environment and self-learning,without any estimation of primary traffic.The simulation results show that compared with the traditional channel selection algorithms,the proposed algorithm can improve the throughput of the secondary users significantly,and when the primary traffic statistics are changed,it can attain convergence automatically,which could be an attempt to the future intelligent cognitive radio systems.

关 键 词：认知无线电网络 频谱感知 强化学习 信道选择 吞吐量 

分 类 号：TN92[电子电信—通信与信息系统]