Research On Multi-parameter Joint Monitoring Technology In Multicore Space Division Multiplexing Transmission System Based On Machine Leaning

The space division multiplexing optical communication system based on multi-core optical fiber can use the space dimension to load information,and can rapidly increase the capacity in various application scenarios such as data center,access network,and long-distance transmission.It is an important part of the future large-capacity optical fiber communication systems.In the current optical communication system based on digital signal processing,the monitoring of transmission parameters such as modulation format and baud rate is very important to realize large-capacity flexible optical network.Accurate identification of it helps to accurately match the receiving end algorithm.Effectively compensate transmission link damage and improve transmission performance.However,compared with the traditional single-mode fiber,the multi-core fiber introduces crosstalk between cores and presents obvious random dynamic change characteristics,which has an adverse effect on the real-time estimation of multi-parameters in the multi-core space division multiplexing system.It is necessary to incorporate crosstalk into the multi-parameter monitoring range,and realize efficient identification of modulation format and baud rate under interference of different crosstalk levels.In order to overcome the interference of dynamic crosstalk in the multi-core space division multiplexing system,real-time monitoring of multiple parameters of the space division multiplexing system under different levels of crosstalk interference is achieved.In the short-distance multi-core space division multiplexing system,this paper proposes a method based on The multi-parameter joint monitoring technology solution of machine learning.The main research contents are as follows:(1)Propose a multi-parameter joint monitoring scheme of multicore space division multiplexing system based on convolutional neural network.The scheme is based on the time series signal received by the receiving end and uses the classification neural network to first identify the modulation format and baud rate of the signal,and then selects different crosstalk regression networks according to the signal modulation format and baud rate and monitors the modulation format and baud rate at the same time.Rate and crosstalk level changes.(2)Build a multicore direct modulation and direct detection system and realize 100%recognition accuracy of modulation format in the multicore direct modulation and direct detection system and achieve a recognition accuracy of more than 99.75% for the baud rate.For OOK signals the crosstalk regression neural network achieves a crosstalk estimation with an average absolute error of 0.16 dB when the crosstalk level interval is changed by1 d B.(3)Build a multicore coherent system and achieve 100% recognition accuracy of modulation formats in the multicore coherent system,achieve a recognition accuracy of more than 98.88% for the baud rate,and a regression neural network for the crosstalk of QPSK signals the crosstalk estimation with an average absolute error of 0.93 d B is achieved when the crosstalk level interval is changed by 1dB.