Optical nonlinear equalization method based on transfer learning

Abstract

The invention provides an optical nonlinear equalization method based on transfer learning. Trained initialized neural network parameters are migrated to a neural network of each channel through transfer learning, and each channel is assisted to quickly establish the neural network equalizer, so that quick modeling is realized, and the resource overhead, such as less new training data and less iterative compensation, is reduced. Meanwhile, once the channel state is changed, such as the optical power and the transmission distance, the nonlinear phase noise is correspondingly changed, and at themoment, if each channel is retrained respectively, huge expenditure is also corresponding. At the moment, by initializing migration of neural network parameters and supplementing a small amount of new data, rapid neural network remodeling is realized, and then updated parameters are migrated to each channel for updating, so that the response capability to channel change is improved. According tothe invention, nonlinear equalization efficiency under different channels is improved, and high tolerance to optical fiber nonlinearity is maintained.

Description

technical field [0001] The invention relates to a nonlinear equalization technology of a multi-channel optical fiber communication system. Background technique [0002] With the continuous emergence of emerging Internet services such as social networks, cloud computing, and virtual reality, the amount of data generated and the demand for communication capacity are growing exponentially. Government agencies, large enterprises, Internet companies, etc. are actively building their own data centers, and the optical interconnection between data centers and even ultra-large-scale data centers has become a hot research issue. The schemes of direct detection system and coherent detection system using intensity modulation or optical I / Q modulator at the transmitting end emerge in endlessly. Based on single-step linear filtering, Volterra filter, and Kramers-Kronig transceiver schemes, great progress has been made in the compensation of beat frequency noise between signals. The 100G...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to improve the tolerance of the existing multiplexing optical fiber transmission system to the nonlinear effect of the optical fiber, and overcome the problem that the existing deep neural network cannot efficiently realize the nonlinear equalization of the optical fiber under multi-channel

Images