Machine Learning-Driven Optimization of Physical Layer Signal Processing in High-Speed Networks
DOI:
https://doi.org/10.71222/2wzq7083Keywords:
machine learning, physical layer, high-speed networks, signal processing, deep learning, adaptive communicationAbstract
The rapid evolution of high-speed communication networks, including 5G/6G, high-speed Ethernet, and satellite systems, has posed significant challenges to physical layer (PHY) signal processing. Traditional analytical methods often struggle with dynamic channel conditions, complex interference, and computational constraints. This review explores the integration of machine learning (ML) techniques for optimizing PHY signal processing, highlighting supervised, unsupervised, and reinforcement learning approaches, as well as deep learning architectures such as CNNs, RNNs, and Transformers. The discussion covers model training strategies, including offline and online adaptive learning, and examines optimization objectives such as bit error rate reduction, spectral efficiency enhancement, and energy efficiency improvement. Representative case studies across high-speed network scenarios demonstrate the practical benefits of ML-driven PHY optimization. Additionally, the paper addresses challenges including data acquisition, model complexity, interpretability, robustness, and security, while outlining potential future directions such as federated learning, edge intelligence, adaptive signal processing, multimodal signal fusion, and quantum machine learning. Overall, ML provides a versatile and adaptive framework that complements traditional algorithms, enabling robust and efficient PHY performance in complex network environments.
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