Dynamic adversarial jamming-based reinforcement learning for designing constellations

Abstract

To resist various types of jamming in wireless channels, appropriate constellation modulation is used in wireless communication to ensure a low bit error rate. Due to the complexity and variability of the channel environment, a simple preset constellation is difficult to adapt to all scenarios, so the online constellation optimization method based on Reinforcement Learning (RL) shows its potential. However, the existing RL technology is difficult to ensure the optimal convergence efficiency. Therefore, in this paper, Dynamic Adversarial Interference (DAJ) waveforms are introduced and the DAJ-RL method is proposed by referring to adversarial training in Deep Learning (DL). The algorithm can converge to the optimal state quickly by self-adaptive power and probability direction of dynamic strong adversary of DAJ. In this paper, a rigorous theoretical proof of the symbol error rate is given and it is shown that the method approaches the mathematical limit. Also, numerical and hardware experiments show that the constellations generated by DAJ-RL have the best error rate at all noise levels. In the end, the proposed DAJ-RL method effectively improves the RL-based anti-jamming modulation for cognitive electronic warfare.