A Power Allocation Algorithm for Underwater Acoustic Communication Networks Based on Reinforcement Learning

The underwater acoustic communication networks are not only self-adaptive but also intelligent. Based on this, this paper studies the resource allocation optimization problem in the underwater multi-node communication network. The present work firstly introduces the idea of reinforcement learning in intelligent control, transforms the resource optimization problem in the underwater acoustic communication networks into a reinforcement learning model, then analyzes the convergence of the model, and finally develops a distributed resource allocation algorithm based on reinforcement learning to improve the network service quality. The simulation results show that the algorithm can adaptively adjust the power value according to the environment, and the convergence speed is fast. It can be used as an attempt to intelligentize the underwater acoustic communication networks in the future.