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Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach
Author(s) -
Huang Luying,
Xu Tao,
Chen Xueqiang,
Xu Yitao,
Zhang Xiao,
Fang Gui
Publication year - 2021
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.4243
Subject(s) - relay , jamming , relay channel , reinforcement learning , computer science , node (physics) , channel (broadcasting) , selection (genetic algorithm) , joint (building) , computer network , markov decision process , throughput , communications system , markov process , wireless , telecommunications , engineering , artificial intelligence , mathematics , architectural engineering , power (physics) , statistics , physics , structural engineering , quantum mechanics , thermodynamics
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.