An End-to-end Learning Framework for Joint Compensation of Impairments in Coherent Optical Communication Systems

The application of machine learning techniques in Coherent Optical Communication (COC) systems has gained increasing attention in recent years. One representative and successful application is to employ neural networks to compensate the signal impairments of devices in the COC system. However, existing studies usually concentrate on each individual device or one impairment, the various impairments sourced from multiple devices (e.g., non-linear distortion, memory and crosstalk effects) are not well investigated. More importantly, due to the impairment isolation caused by frequency offset, traditional studies only compensate the impairments of transmitter or receiver individually. In this paper, we consider a more practical and challenging experimental setup environment: joint compensation of multiple impairments associated with all devices of transmitter and receiver simultaneously. To this end, we propose an end-to-end compensation framework from the transmitter to the receiver in COC systems with three associated modules: an auxiliary channel neural network for impairment modeling, a pre-compensation neural network deployed in the transmitter, and a post-compensation neural network deployed in the receiver. Different from previous works, the proposed framework not only allows modeling all impairments of multiple devices, but also provides a new venue for joint compensation of the transmitter and receiver simultaneously. The solution has been successfully verified by the high baud rate (120Gbaud) coherent optical professional test platform and shows impressive optical Signalto-Noise Ratio (SNR) gains.