Intelligent Power Allocation in Underwater UVLC Using Deep Reinforcement Learning and MIMO-NOMA Under Realistic Channel Models

This study proposes a deep reinforcement learning (DRL)-based power allocation framework for underwater MIMO-NOMA visible light communication (UVLC) systems, rigorously validated under real-world turbulence profiles calibrated with empirical oceanographic data. The framework integrates depth-dependent turbulence modeling (Kolmogorov and Anderson spectra) to simulate dynamic underwater channels, addressing salinity gradients, temperature variations, and non-stationary turbulence. A multi-objective reward function, optimized via systematic grid search and Pareto analysis, balances spectral efficiency, BER minimization, and energy efficiency while penalizing forward error correction (FEC) of 3.8× 10-3. Hardware-aware simulations demonstrate that the proposed Q-learning DRL strategy achieves a 50% BER reduction (1.8× 10-3) vs. 3.6× 10-3), 7 dB SNR gain, and 30% throughput improvement over conventional water-filling and genetic algorithms, with