A signal equalization method and device for optical communication

Abstract

The embodiment of the invention provides a signal equalization method and device for optical communication, and the method comprises the steps: carrying out preprocessing of a to-be-equalized signal,and obtaining a target signal; based on a trained signal equalization model, carrying out equalization processing on the target signal to obtain an equalization result of the target signal so as to obtain an equalized signal, wherein the trained signal equalization model is constructed on the basis of an XGBoost model, and is obtained by training a sample signal which has a plurality of characteristics and is labeled with a label. According to the embodiment of the invention, the machine learning method based on the adaptive tree model is applied to signal equalization; intelligent equalization of the received signals in various optical communication scenes is realized through employing the XGBoost model, the intelligentization and automation of the signal equalization are realized, more accurate equalization of the signals is realized, and better performance is realized through employing the lower complexity.

Description

technical field [0001] The invention relates to the technical field of optical communication digital signal processing, in particular to a signal equalization method and device for optical communication. Background technique [0002] In recent years, short-distance optical transmission has become a research hotspot in the field of optical communication, especially in the field of data center (DataCenter, DC for short) optical interconnection. Since short-distance optical communication has commercial requirements of low cost and low power consumption, it is necessary to use a low-cost Intensity Modulation Direct Detection (IM-DD) transceiver. However, the low-order modulation of the signal and the susceptibility to fiber damage limit the transmission capacity and distance of the IM-DD system. [0003] In the existing technology, feed forward equalization (Feed Forward Equalization, FFE for short), decision feedback equalization and nonlinear Volterra equalizer can all be use...

AI Technical Summary

Problems solved by technology

However, these equalization methods with high computational complexity and limited adaptability achieve more general performance, leaving much room for improvement

At present, machine learning has been widely used in the field of optical communication, including optical performance monitoring and digital coherence detection, especially Deep Neural Networks (DNN) has been applied to the equalization of damaged signals, but with the increase of the number of features , resulting in a higher complexity of the DNN network

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