Secure Underwater Distributed Antenna Systems: A Multi-Agent Reinforcement Learning Approach

Abstract

Dear Editor, Underwater distributed antenna systems (DAS) are stationary infrastructures consisting of multiple geographically distributed antenna elements (DAEs) which are interconnected through high-rate backbone networks [1]. Compared to centralized systems, the DAS could provide a larger coverage area and higher throughput for underwater acoustic (UWA) transmissions. In this work, exploiting the low sound speed in water, a multi-agent reinforcement learning (MARL)-based approach is proposed to secure underwater DAS against eavesdropping at the physical layer. Specifically, the theoretical secrecy rate is firstly derived for time-slotted UWA networks (UWANs) considering the large propagation delays. Furthermore, we investigate the long-term sum secrecy rate optimization problem under the MARL framework, where each DAE learns its optimal transmission strategy online. Simulation results show that the proposed method achieves higher secrecy performance compared to competing benchmark methods.