OIRS-Assisted VLC Channel Optimization Against UAV Blockage Based on Two-Stage Machine Learning Framework

The dynamic blocking caused by unmanned aerial vehicle (UAV) severely reduces the channel reliability of visible light communication (VLC), where conventional multipath diversity is unavailable. To address this, we propose an optical intelligent reflecting surface (OIRS)-assisted robust VLC framework against UAV blockage. To simulate and quantify blockage, we establish a unified channel-optical field-blocking model integrating: (i) a Gaussian-approximated optical power distribution for OIRS mirror units, derived from far-field-filtered Lambertian emissions; (ii) a probabilistic blockage model using Poisson point processes to quantify UAV-induced blockages. We formulate the optimization of light field distribution on OIRS as a dynamic optimization problem to maximize the user’s received power under blockage constraints by adjusting the deflection angle of VLC access point (AP). We propose a two-stage machine learning approach to solve this dynamic optimization problem. In the offline stage, we employ greedy combinatorial optimization to precompute diverse OIRS beam configurations for building a configuration database. In the online stage, we first detect occluded mirror units using sparse recovery, and then dynamically adjust the beam aiming center based on real-time conditions through database guidance selection. The simulation results show that our solution can significantly improve the actual anti-blocking performance under high-density mirror unit configuration compared to static state, random enable, and gradient descent.