Deep Reinforcement Learning for Enhancing the Secrecy of a MU-MISO UOWC Network

In this paper, we propose a Deep Reinforcement Learning (DRL) framework to optimize the secrecy performance of a Multi-User (MU)-Multiple-Input Single-Output (MISO) Underwater Optical Wireless Communication (UOWC) system. The network consists of several light-emitting diodes connected with various underwater users through optical beams. The legitimate transmission is threatened by several eavesdroppers attempting to overhear the confidential message sent to each user. Thus, digital precoding is employed to cancel the inter-user interference and maximize the per-user secrecy rate and, consequently, the secrecy sum rate (SSR). Leveraging the developed DRL algorithm, the MU-MISO precoding matrix is optimized for enhancing the system's SSR. Numerical results show the superiority of the proposed DRL framework compared to the baseline zero-forcing and random pre coding schemes, even with corrupted CSI at the transmitter due to seawater dynamics and estimation errors.