Joint relay and channel selection against mobile and smart jammer: A deep reinforcement learning approach

IET Communications

Fei Song¹  Yuping Gong¹  Hao Han¹  Wen Li¹  Hongcheng Yuan¹  Xiaojing Chu¹  Ximing Wang² 

[1] College of Communications Engineering Army Engineering University of PLA Nanjing China;College of Information and Communication National University of Defense Technology Wuhan China;

关键词: Markov processes;    Electromagnetic compatibility and interference;    Radio links and equipment;    Markov processes;    Communications computing;    Reinforcement learning;   

DOI  :  10.1049/cmu2.12257

来源: DOAJ

【 摘 要 】

Abstract This paper investigates the joint relay and channel selection problem using a deep reinforcement learning (DRL) algorithm for cooperative communications in a dynamic jamming environment. The latest types of jammers include the mobile and smart jammer that contains multiple jamming patterns. This new type of jammer poses serious challenges to reliable communications such as huge environment states, tightly coupled joint action selections and real‐time decision requirements. To cope with these challenges, a DRL‐based relay‐assisted cooperative communication scheme is proposed. In this scheme, the joint selection problem is constructed as a Markov decision process (MDP) and a double deep Q network (DDQN) based anti‐jamming scheme is proposed to address the unknown and dynamic jamming behaviors. Concretely, a joint decision‐making network composed of three sub‐networks is designed and the independent learning method of each sub‐network is proposed. The simulation results show that the user agent is able to anticipate the jammer behaviors and elude the jamming in advance. Furthermore, compared with the sensing‐based algorithm, the Q learning‐based algorithm and the existing DRL‐based anti‐jamming approaches, the proposed algorithm maintains a higher average normalized throughput.

【 授权许可】

Unknown