未知环境中基于图型博弈和Multi-Q学习的动态信道选择算法

研究了分布式无线网络中，没有任何信息交换、也没有环境变化先验知识情况下的动态信道接入算法。运用图型博弈模型对用户的实际拓扑进行建模分析，证明了此博弈模型存在纯策略纳什均衡并且此纳什均衡是全局最优解。同时，采用multi-Q学习求解模型的纯策略纳什均衡解。仿真实验验证了multi-Q学习能获得较高的系统容量以及在图型博弈模型中用户的效用主要由节点的度决定，而与用户数量无直接关系。

Dynamic channel selection in unknown environment based on graphical game and multi-Q learning

For the problem of dynamic channel selection in unknown distributed environment without a priori knowledge and information exchange, multi-Q learning was proposed. The dynamic channel selection problem was formulated the existence of pure strategy Nash equilibrium in graphical game was proved. At the same time, the pure strategy Nash equilibrium was proved to be global optimal solution. Simulation results show that multi-Q learning achieves high system capacity and utility of users in the graphical game are determined mainly by the degree of the node without direct relationship to the number of users.