Beam Power Optimization in FSO Links Using Reinforcement Learning and SLM Control

This work presents a reinforcement learning (RL) approach for optimizing beam intensity in free-space optical communication (FSOC) links under thermal turbulence. A Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controls Zernike phase parameters on a high-resolution spatial light modulator (SLM) to maximize received power. Experiments at 72°F, 240°F, and 280°F demonstrate consistent convergence and robust performance across varying turbulence levels, with convergence achieved in under 2.7 minutes and average power gains of 2–2.7 dB relative to a flat phase. The method is designed for next-generation low Earth orbit (LEO) optical links, where it can be applied to compact ground station terminals or feeder links to provide real-time adaptive phase correction and maintain stable communication under dynamic channel conditions.