Nonlinear equalization method and system based on feature construction of polynomial mapping

Abstract

The invention discloses a nonlinear equalization method and system based on polynomial mapping feature construction. The method comprises the following specific steps: S1, carrying out normalization processing on a receiving end sampling signal Xt to obtain a receiving end sampling signal X normalized by an equalizer; s2, nonlinear features of signals are introduced according to a polynomial method, and a feature sequence Xf is reconstructed; and S3, adjusting the tap coefficient of the linear equalizer on the training set by using an adaptive algorithm to obtain a trained linear equalizer, inputting a signal needing to be equalized into the equalizer, and judging the output of the equalizer to realize channel equalization. Compared with a traditional nonlinear equalizer, the linear equalizer has the advantages that the huge calculated amount in the process of adaptively adjusting the weight of each tap of the equalizer is greatly relieved through feature construction, and the performance of the same order of magnitude as that of the nonlinear equalizer can be obtained.

Description

technical field [0001] The invention belongs to the technical field of optical communication, and in particular relates to a nonlinear equalization method and system based on polynomial mapping feature construction. The invention can be applied to equalize nonlinear damage in an optical fiber communication system. Background technique [0002] Due to the vigorous development of various new applications in recent years, technologies such as the fifth generation mobile communication system (5G), the Internet of Things, cloud computing, high-definition video services, and virtual reality applications have promoted the explosive growth of data traffic. As the main body of traffic, optical fiber The upgrade scheme of communication system capacity has been widely concerned, and the optical network with large capacity and low cost is the future development direction. In the communication optical network, the Data Center Interconnect (DCI) network belongs to the short-distance optic...

AI Technical Summary

Problems solved by technology

However, the computational complexity of the Volterra equalizer is high, requiring hundreds of features to achieve satisfactory performance

Even after adopting a series of complexity reduction schemes, it is difficult to obtain an equalizer with good performance and simple calculation method

The simplification schemes based on the Volterra equalizer that have been proposed at present generally include the following types: Wei Jinlong, Lam Cedric et al. propose setting thresholds and removing taps with tap coefficients below the threshold (Low Complexity DSP for High Speed ​​Optical AccessNetworking, published Applied Sciences), but the selection of the threshold needs to be cautious. If the threshold is too large, a large number of taps will be cut off, and it will not be possible to retain enough features for signal classification. If the threshold is too small, the computational complexity cannot be reduced.

In 2020, Yukui Yu, Hoon Kim et al. (Low-complexity nonlinear equalizer based on absolute operation for C-band IM / DD systems, published in Opt Express and Nonlinear Equalizer Based on Absolute Operation for IM / DD System Using DML, published in IEEE PhotonicsTechnology Letters) and Qianwu Zhang team (An Improved Volterra NonlinearEqualizer for 50Gb / s PAM4IM / DD Transmission with 10G-Class Optics, published in ACP) both proposed to replace the product operation in the Volterra equalizer with an absolute value operation, but replace the product operation with The method of absolute value calculation will cause the performance of the Volterra equalizer to decrease

However, the existing schemes based on machine learning algorithms for equalizing nonlinear interference have high complexity and cannot meet the low-cost and low-power requirements of the IMDD system (An Overview on Application of Machine Learning Techniques in Optical Networks, published in 2019 on IEEE Communications Surveys & Tutorials)

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