EWA博弈抽象的认知无线电网络信道选择

通过协作频谱感知对信道可用性进行分析，构建网络可用信道的优先度表. 利用该优先度表，提出一种基于EWA学习博弈模型的信道选择算法. 与基于学习自动机算法和无悔学习算法对比的仿真结果表明，该算法可通过历史经验的学习选择对认知用户可用性最优的信道，能提高系统的有效吞吐量，并获得更好的资源分配公平性.

Channel Selection Based on EWA Game Abstraction in Cognitive Radio Network

A priority table of available channels in a network is structured by analyzing channel availability with cooperative spectrum sensing. Using this table, a channel selection learning algorithm based on the experience-weight attraction (EWA) game learning is proposed. Simulations are carried out to compare the learning automata-based algorithm with no-regret learning algorithm. The results show that, by learning historical experience, the algorithm can select channels with the best availability for cognitive users, increase the effective system throughput, and have better equity in resource allocation.