Transfer Reinforcement Learning-Based Power Control for Anti-Jamming in Underwater Acoustic Communication Networks - Liejun Yang; Yi Chen; Hui Wang (Engineering Proceedings, 2025)
Underwater acoustic communication networks (UACNs) play a critical role in ocean environmental monitoring, maritime rescue, and military applications. However, they are highly susceptible to performance degradation due to narrow bandwidths, long propagation delays, and severe multipath effects, especially adversarial jamming attacks. Traditional anti-jamming techniques struggle to adapt to the dynamic nature of underwater acoustic channels effectively. To address this issue, an anti-jamming power control and relay optimization method was developed based on transfer reinforcement learning. By introducing relay nodes, the reliability of jammed communication links is enhanced. Transfer learning was used to initialize Q-values and strategy distributions and accelerate the convergence of reinforcement learning in the underwater communication environment, thereby mitigating the inefficiency of random exploration in the early stages. The proposed method optimizes the transmission power and relay selection to improve the signal-to-interference-plus-noise ratio (SINR) and reduce the bit error rate (BER). Simulation results demonstrated that the proposed method significantly enhanced the anti-jamming performance and communication efficiency of underwater acoustic communication even in complex interference scenarios.