Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems

Abstract

With the growing maritime economy, ensuring the quality of communication for maritime users has become imperative. The maritime communication system based on nearshore base stations enhances the communication rate of maritime users through dynamic resource allocation. A virtual queue-based deep reinforcement learning beam allocation scheme is proposed in this paper, aiming to maximize the communication rate. More particularly, to reduce the complexity of resource management, we employ a grid-based method to discretize the maritime environment. For the combinatorial optimization problem of grid and beam allocation under unknown channel state information, we model it as a sequential decision process of resource allocation. The nearshore base station is modeled as a learning agent, continuously interacting with the environment to optimize beam allocation schemes using deep reinforcement learning techniques. Furthermore, we guarantee that grids with poor channel state information can be serviced through the virtual queue method. Finally, the simulation results provided show that our proposed beam allocation scheme is beneficial in terms of increasing the communication rate.