Multi-domain collaborative anti-jamming based on multi-agent deep reinforcement learning - Biao ZHANG; Ximing WANG; Yifan XU; Wen LI; Hao HAN; Songyi LIU; Xueqiang CHEN (物联网学报)
Dynamic transmission requirements and the limited cache space bring great challenges to wireless data transmission in the malicious jamming environment.Aiming at the above problems, a collaborative anti-jamming channel selection and data scheduling joint decision method for distributed internet of things was studied from the perspective of frequency domain and time domain.A data transmission model based on multi-user Markov decision process was constructed and a collaborativeanti-jamming joint-channel-and-data decision algorithm based on multi-agent deep reinforcement learning was proposed.Simulation results show that the proposed algorithm can effectively avoid the malicious jamming and the co-channel interference.Compared with the comparison algorithm, the network throughput is significantly improved, and the number of packet dropout is significantly reduced.