Intelligent eye diagram analysis method by use of deep learning based on convolutional neural network

Abstract

The invention discloses an intelligent eye diagram analysis method by use of deep learning based on a convolutional neural network (CNN), which relates to the technical field of optical communication. A CNN module is built and trained to analyze the performance of an eye diagram. The method includes the following steps: acquiring an eye diagram training data set; preprocessing an eye diagram; training the CCN module, and extracting features; inputting the preprocessed eye diagram needing analysis into the trained CNN module for mode identification and performance analysis; and outputting the analysis result. By applying the deep learning technology based on the convolutional neural network to eye diagram analysis, the problem that original data cannot be processed directly without manual intervention in the traditional eye diagram performance analysis is solved. By using the CNN, intelligent and automatic analysis of the original image information of an eye diagram is realized. The CNN module can be embedded into a test instrument as an eye diagram software processing module of an oscilloscope or as an eye diagram analysis module of simulation software to carry out intelligent signal analysis and performance monitoring.

Description

technical field [0001] The invention relates to the technical field of optical communication, in particular to a deep learning intelligent eye diagram analysis method based on a convolutional neural network. Background technique [0002] Machine learning (ML) techniques provide powerful tools to solve problems in many fields such as natural language processing, data mining, speech recognition, and image recognition. At the same time, machine learning technology has also been widely used in the field of optical communication, which has greatly promoted the development of intelligent systems. Current research mainly focuses on the use of different machine learning algorithms for optical performance monitoring (OPM) and nonlinear damage compensation. The machine learning algorithms used include expected maximum (EM), random forest, backpropagation artificial neural network (BP -ANN), K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), etc. However, all of the above m...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to apply deep learning technology to the field of optical communication, provide an intelligent and reliable deep learning intelligent eye diagram analysis method based on convolutional neural network, and solve the problem that the original image data cannot be directly processed in the traditional eye diagram performance analysis. The disadvantages of manual intervention are required, and the intelligence and automation of performance analysis on the original image of the eye diagram are realized

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