Machine Learning-Assisted Mitigation of Optical Multipath Interference in PAM4 IM-DD Transmission Systems - Wenxin Cui; Jiahao Huo; Jin Zhu; Jianlong Tao; Peng Qin; Xiaoying Zhang; Haolin Bai (Photonics)
This paper aims to mitigate multipath interference (MPI) in intensity modulation with direct detection (IM-DD) systems using machine learning techniques, specifically for four-level pulse amplitude modulation (PAM4) systems. We propose a machine learning-assisted MPI mitigation scheme, called KNN-aided SVM+RF-M. In this scheme, KNN-aided SVM serves as a soft decision algorithm that adapts the decision threshold to signal amplitude fluctuations, improving the decision accuracy for MPI-affected PAM4 signals. By replacing the original hard decision in the RF-M algorithm with KNN-aided SVM, we mitigate the error transfer problem inherent in RF-M. MPI mitigation is then achieved through MPI estimation and noise value cancellation methods applied to signals after soft decision processing. Our proposed scheme is validated in a 28 GBaud PAM4-DD transmission system, and the simulation results show that our proposed scheme can improve SIR tolerance by 2 dB and receiver sensitivity by about 1 dB at the 7% HD-FEC threshold compared to the original RF-M scheme.