Learning-Aided Joint Beam Divergence Angle and Power Optimization for Seamless and Energy-Efficient Underwater Optical Communication

Integrating underwater optical wireless communication (UOWC) with marine applications, such as underwater sensors, buoys, and marine surface vehicles (MSVs), requires the aligning and maintaining of the optical beam between the transmitter and receiver for point-to-point (P2P) UOWC during the data transmission. An additional issue is the difficulty in exchanging batteries for marine applications because of the relatively high costs and risks compared with battery exchanging in terrestrial applications. This study seeks to resolve these issues via joint optimization of the beam divergence angle and transmission power level in an underwater sensor (i.e., transmit node) to maintain a seamless connection with an MSV (i.e., receive node) while minimizing the battery consumption of the sensor. In this regard, we adopt a hybrid underwater acoustic-optical communication system, where acoustic and optical communications are used for low-rate control data transmission and high-rate sensing data transmission, respectively. Under this framework, we propose a two-phase deep reinforcement learning (TPDRL) algorithm considering two agents (inner and outer) that determine different actions using an underwater sensor. Specifically, the primary role of the outer agent is to choose a transmission power level based on the long-term signal-to-noise ratio (SNR) between the underwater sensor and MSV. Next, the inner agent finds the beam divergence angle for the given transmission power (selected from the outer agent) based on the short-term instantaneous SNR. Simulation results demonstrate that the proposed TPDRL algorithm enables seamless and energy-efficient P2P UOWC, performing better than the algorithm with only the inner agent and other existing algorithms.