An Improved CSMA/CA Protocol Anti-Jamming Method Based on Reinforcement Learning

Abstract

The CSMA/CA algorithm uses the binary backoff mechanism to solve the multi-user channel access problem, but this mechanism is vulnerable to jamming attacks. Existing research uses channel-hopping to avoid jamming, but this method fails when the channel is limited or hard to hop. To address this problem, we first propose a Markov decision process (MDP) model with contention window (CW) as the state, throughput as the reward value, and backoff action as the control variable. Based on this, we design an intelligent CSMA/CA protocol based on distributed reinforcement learning. Specifically, each node adopts distributed learning decision-making, which needs to query and update information from a central status collection equipment (SCE). It improves its anti-jamming ability by learning from different environments and adapting to them. Simulation results show that the proposed algorithm is significantly better than CSMA/CA and SETL algorithms in both jamming and non-jamming environments. And it has little performance difference with the increase in the number of nodes, effectively improving the anti-jamming performance. When the communication node is 10, the normalized throughputs of the proposed algorithm in non-jamming, intermittent jamming, and random jamming are increased by 28.45%, 21.20%, and 17.07%, respectively, and the collision rates are decreased by 83.93%, 95.71%, and 81.58% respectively.