Multi-Channel Anti-Jamming Transmission Method Based on Distributed Deep Reinforcement Learning

In response to the challenge posed by the inherent transmission limitations of single-channel anti-jamming data transmission methods, which struggle to meet the demands of high-intensity data transmission, this study explores the problem of multi-channel anti-jamming data transmission in a multi-user communication network operating under malicious jamming, with dynamic transmission needs. Communication users dynamically switch between parallel and diversity transmission based on their transmission requirements. A multi-channel anti-jamming transmission method based on distributed deep reinforcement learning is proposed. Simulation results demonstrate that the proposed algorithm achieves higher normalized throughput and lower average transmission latency.