Joint Channel and Power Optimisation for Multi-User Anti-Jamming Communications: A Dual Mode Q-learning Approach

In view of the anti-tracking-jamming problem, traditional online learning methods usually cannot analyse the jamming behaviour, and find an effective way to prevent the jamming attacks. To cope with these challenges, a novel communication/deception dual mode mechanism is proposed in this paper. Deception users are selected to send high-power signal for jamming attraction, and form collaborative relationships with communication users. The corresponding collaborative anti-jamming model is then constructed as a Markov game to analyse the multi-agent decision. Based on that, a joint channel and power optimisation for multi-user anti-jamming communications based on dual mode Q-learning scheme is proposed. Compared with two traditional online learning algorithms, the proposed DCAJ-QL algorithm effectively achieves 146.5% and 80.4% higher maximum communication rate under tracking jamming conditions, and achieves 40.7% and 53.6% higher maximum communication rate under fixed jamming conditions.