基于多智能体强化学习的动态频谱分配方法

针对认知无线电网络中多个异质用户具有不同的服务质量(QoS)要求，提出一种基于多智能体强化学习的动态频谱分配方法。该方法从用户满意度角度出发，以用户体验质量(QoE)作为系统的评价指标，构建多个虚拟智能体，模拟多个用户以合作方式与环境进行交互学习，融合各个用户的学习和频谱决策结果，实现频谱资源优化分配。仿真结果表明，在未知主要用户使用规律和信道动态特性条件下，相比基于传统强化学习的动态频谱分配方法，提出的方法能有效提高次用户的QoE，降低用户间的冲突概率。

Dynamic spectrum allocation method based on multi-agent reinforcement learning

Multiple heterogeneous spectrum users require different Quality of Service(QoS) in cognitive radio networks. A dynamic spectrum allocation method is proposed based on multi-agent reinforcement learning. In order to improve the satisfaction of spectrum users, the proposed method is evaluated by the Quality of Experience(QoE) of spectrum users instead of QoS. Multiple virtual agents are established to simulate spectrum users to learn interactively with environment in a cooperative way, and the optimal spectrum allocation can be obtained by integrating their learning and spectrum decision results. Simulation results show that the proposed method can obtain higher QoE performance of secondary users than those methods based on the traditional reinforcement learning. The probability of collision between spectrum users also can be reduced in the proposed method without any information about the usage rules of primary users and dynamic characteristics of channels.