Abstract Underwater visible light communication (UVLC) has emerged as a promising solution for high-speed and low-latency underwater communication; however, its performance is fundamentally constrained by severe channel impairments and the limited energy availability of underwater sensor nodes. In this paper, an intelligent reflecting surface (IRS)-assisted UVLC framework with energy harvesting is proposed, where both spatial and temporal resources are jointly optimized to enhance system reliability and sustainability. Specifically, the IRS is adaptively partitioned into two groups to simultaneously support energy harvesting and data transmission, while the transmission frame is dynamically divided to determine the optimal harvesting duration. Unlike existing works that rely on static IRS allocation and static harvesting time, the proposed scheme formulates a joint optimization problem to maximize the ergodic capacity under a minimum energy constraint. By using Karush-Kuhn-Tucker (KKT) conditions, closed-form expressions for the optimal harvesting time and IRS partitioning are obtained, and key insights are provided on the trade-off between energy harvested and information transfer. Furthermore, the closed-form expressions for ergodic capacity, harvested energy, and outage probability are derived using practical underwater channel conditions, such as absorption, scattering, turbulence, different link distances, etc. The simulation results are presented to demonstrate the effectiveness of the proposed framework over the conventional schemes.
Indu Bala (Mon,) studied this question.