An Investigation of Cross-Phase Modulation and Machine Learning Based Alleviation of Nonlinear Effects in a DP-QPSK Based DWDM System
Corresponding author: M. N. Mahalakshmi
Abstract
Dense Wavelength Division Multiplexing (DWDM) systems with Dual-Polarization Quadrature Phase-Shift Keying (DP-QPSK) technology are enabling current optical fiber communication systems in Metropolitan Area Networks and Long-haul networks. The impact of the nonlinear impairments caused by the Kerr effect is more detrimental to the performance of DWDM systems. Among these impairments, the dominant impairment is Cross-Phase Modulation (XPM). In this paper, a nine-channel coherent DP-QPSK DWDM system with Standard Single Mode Fiber (SSMF) at a bit rate of 112 Gb/s per channel is simulated in OptiSystem v18. The study examines how XPM affects performance, specifically focusing on polarization crosstalk and phase noise for different channel spacings and transmission distances up to 900 km. The alleviation of nonlinear effects is demonstrated using two Machine Learning (ML) models: Bidirectional Long Short-Term Memory (Bi-LSTM) and Transformer. The efficacy of these machine learning techniques in mitigating nonlinear effects is evaluated against Digital Back Propagation (DBP). The findings demonstrate that the Transformer model outperforms both DBP and Bi-LSTM.
Keywords:
Cross-Phase Modulation, DP-QPSK, DWDM, nonlinear effectsDownloads
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