An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning

Aiming at the problem of mutual interference between nodes and external malicious jamming in wireless communication networks, this paper proposes an intelligent communication anti-jamming algorithm based on distributed double Q-learning. First of all, the proposed algorithm anti-jamming problem is modeled as a time-frequency two-dimensional optimization problem. The decoupled relationship between subframe and channel is used. Each node uses two Q-learning. According to the automatically received feedback signal, users choose their subframe and channel to avoid malicious external jamming and mutual interference between users. It maximizes the sum of all user throughput. Simulation results show that the proposed algorithm can effectively shorten the convergence time and improve the performance of the system.