In this article, a joint relay and channel selection problem is investigated in multi-relay anti-jamming communication system. Considering the jamming pattern and the relay node (RN) location distribution are unknown, the relay and channel selection problem is formulated as Markov decision processes (MDPs). Different from the existing research on anti-jamming communication, in this article, the source node (SN) and all RNs are considered as agents who collaboratively learn the environment and make anti-jamming decisions. A reinforcement-learning-based joint relay and channel selection method is proposed to achieve relay-aided anti-jamming communication. Specifically, the SN tries to make the optimal selection of relay which is under the least jamming threat, while the RN will access the jamming-free channels. Various simulation results show that the algorithm can quickly learn the unknown changing pattern of the jamming environment, and make the effective joint relay and channel decisions to obtain high communication throughput which is close to the optimal.

中文翻译：

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中继辅助抗干扰系统中的联合中继和信道选择：一种强化学习方法

本文研究了多中继抗干扰通信系统中的联合中继和信道选择问题。考虑到干扰模式和中继节点 (RN) 位置分布未知，中继和信道选择问题被表述为马尔可夫决策过程 (MDP)。与现有的抗干扰通信研究不同，本文将源节点（SN）和所有RNs视为协同学习环境并做出抗干扰决策的代理。提出了一种基于强化学习的联合中继和信道选择方法来实现中继辅助抗干扰通信。具体来说，SN 尝试对受干扰威胁最小的中继进行最佳选择，而 RN 将访问无干扰信道。