Capacity Maximization for Reconfigurable Intelligent Surface-Aided MISO Visible Light Communications

This paper investigates the employment of reconfigurable intelligent surfaces (RISs) to improve asymptotic capacity multiple-input single-output (MISO) visible light communication (VLC) system in case high signal-to-noise (SNR). For RIS-aided MISO-VLC based on mirror array, we regard high-SNR with input subject peak-intensity constraints as a goal and formulate an maximization problem find optimal orientations mirrors. As for non-convex optimization problem, convert it into quadratic programming (QP) hemispherical prove that can be solved by computing maximum eigenvalue equivalent matrix. Simulation results indicate is able improved significantly adopting RIS systems. Meanwhile, observe proper deployment scheme enhance degree improvement through several simulations.