A Machine Learning Aided Reference-Tone-Based Phase Noise Correction Framework for Fiber-Wireless Systems

• Published in: IEEE transactions on machine learning in communications and networking Vol. 2; pp. 888 - 903
• Main Authors: Hao Thng, Guo, Mikki, Said
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: Analog radio-over-fiber; centralized radio access network; deep learning; fiber-wireless; machine learning; microwave photonics; millimeter-wave; Optical fibers; Optical mixing; Optical noise; Optical receivers; Optical transmitters; Phase noise; Wireless communication
• ISSN: 2831-316X; 2831-316X
• DOI: 10.1109/TMLCN.2024.3418748

In recent years, the research involving the use of machine learning in the field of communication networks have shown promising results, in particular, improving receiver sensitivity against noise and link impairment. The proposal of analog radio-over-fiber fronthaul solutions simplifies the overall base station configuration by generating wireless signals at the desired transmission frequency, directly after photodiode heterodyne detection, without requiring additional frequency upconversion components. However, analog radio-over-fiber signals is more susceptible to nonlinear distortions originating from the optical transmission system. This paper explores the use of machine learning in an analog radio-over-fiber link, improving receiver sensitivity in the presence of phase noise. The machine learning algorithm is implemented at the receiver. To evaluate the feasibility of the proposed machine learning based phase noise correction approach, software simulations were conducted to collect data needed for machine leanring algorithm training. Initial findings suggests that the proposed machine-learning-based receiver's can perform close to conventional heterodyned-based receivers in terms of detection accuracy, exhibiting great tolerance against phase-induced noise, with a symbol error rate improvement from <inline-formula> <tex-math notation="LaTeX">10^{-2} </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">10^{-5} </tex-math></inline-formula>, using a relatively simple machine learning algorithm with only 3 hidden layers consisting of fully connected feedforward neural networks.