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.
The advent of neural network (NN)-based autoencoders has enabled joint transceiver optimization via end-to-end (E2E) learning. However, most existing approaches rely on differentiable surrogate channel models, leading to performance mismatch when deployed over real-world channels. Emerging model-free variants alleviate...
Yuan Wei, Chao-Xu Chen, Li Yao et al.· IEEE Transactions on Cogniti...· 0 citations
A reinforcement learning (RL)-driven control framework that operates over a bank of pretrained multi-rate AEs, each corresponding to a distinct compression ratio (CR), aiming to dynamically optimize the trade-off between reconstruction fidelity and signaling overhead.
Maryam Ansarifard, M. Sharma, Georgios Exarchakos et al.· arXiv.org· 0 citations
Most learning-based reconfigurable intelligent surface (RIS) designs assume continuous control or closed-box channel models, limiting physically grounded end-to-end (E2E) training and neglecting practical 1-bit hardware constraints. We propose a physics-informed framework for RIS-assisted MIMO–OFDM that embeds an optic...
Ahmad Faisal Mirza, M. Ouameur, Mohammed Ali Dou et al.· IEEE Transactions on Green C...· 0 citations
The analysis demonstrates that the RL-based approach, enabled by an effective neural network initialization strategy, surpasses traditional methods and ML-based DPD schemes such as DLA and ILA and provides a scalable and efficient solution for compensating pattern-dependent nonlinearities in high-speed optical communic...
Arash Rabiepoor, L. Rusch, Ming Zeng· IEEE Open Journal of the Com...· 0 citations
Low Earth orbit (LEO) satellite-terrestrial communication systems grapple with significant challenges posed by their inherent dynamism and substantial transmission delays. To address these critical issues, this paper proposes a novel hybrid-medium transmission optimization framework that leverages high-altitude platfor...
Yi Huang, Jin Li, Yan-Wen Zhu et al.· IEEE Transactions on Communi...· 0 citations
Reinforcement fine-tuning strategies for adapting Qwen2.5-3B-Base to graduate-level signal mathematical problems from WirelessMATHBench-XL are investigated and whether GSPO or GMPO offer advantages in stability or accuracy over GRPO for signal reasoning tasks is assessed.
Guozheng Sun· 0 citations
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