Machine Learning Classification and Adaptive Power Control for RGB Underwater Visible Light Communication

Underwater visible light communication (UVLC) offers high data rates over acoustic and radio-frequency links but is highly sensitive to bubble-induced scattering. This paper presents an RGB wavelength-division-multiplexed UVLC system simulated in OptiSystem across clear-water, intermediate, and bubble-induced turbidity conditions. Eye-diagram features are classified using XGBoost, achieving 98.75% test accuracy and 96.18% (±2.17%) repeated cross-validation accuracy, statistically validated against SVM and Random Forest. A held-out intermediate-turbidity set, unseen during training, is correctly identified by wavelength with 92.31–70.65% accuracy across 25–75% turbidity. An independently computed Mie scattering calculation shows bubble-induced scattering becomes wavelength-insensitive at realistic bubble sizes, consistent with the classifier’s confusion pattern. A systematic grid search over laser bias and modulation current on the blue channel identifies a distinct optimal operating point for clear-water and bubble-induced conditions, with bubble-induced BER reaching 6.95×10−9 at its optimum, and indicates a sharp, condition-dependent transition in which settings tolerated under clear water drive the bubble-induced link toward its estimated reliability limit. Shannon capacity analysis indicates multi-gigabit-per-second headroom, and comparison with acoustic and radio-frequency alternatives confirms UVLC’s data-rate advantage at short range. The framework connects physical-layer simulation, classification, and characterized adaptive control within a single validated system.