Reinforcement Learning-Guided Transmitter Optimization for Short-Reach IM/DD FSO Systems
We propose a reinforcement-learning (RL) guided transmitter optimization framework for short-reach IM/DD free-space optical (FSO) links that jointly tunes probabilistic shaping (PS), geometric shaping (GS), and pre-equalization (Pre-EQ) filter coefficients using the soft actor-critic (SAC) algorithm. The agent adapts transmitter parameters in a closed-loop manner from measured link-quality feedback, removing reliance on explicit channel models. In a 30-m PS-PAM4 FSO testbed with a 7-tap FIR Pre-EQ at the transmitter and standard offline DSP at the receiver, the agent uses generalized mutual information (GMI) as the reward to coordinate PS/GS with Pre-EQ. Experiments show a peak achievable information rate (AIR) of 194.3 Gb/s at 110 Gbaud and receiver-sensitivity gains of ~1.5 dB over traditional Pre-EQ and ~3 dB over no Pre-EQ, demonstrating the efficacy of closed-loop, model-free transmitter learning for next-generation optical wireless access.