Deep Learning Intelligent Eye Diagram Analysis Method Based on Convolutional Neural Network

Abstract

The invention discloses a deep learning intelligent eye diagram analysis method based on a convolutional neural network, which relates to the technical field of optical communication, wherein the performance analysis of the eye diagram is performed by building and training a convolutional neural network module, including the following steps: obtaining the eye diagram Training data set; preprocessing the eye diagram; training the CNN module for feature extraction; preprocessing the eye diagram to be analyzed and inputting it into the trained CNN module for pattern recognition and performance analysis; outputting the analysis results. The present invention applies deep learning technology based on convolutional neural network to eye diagram analysis, which solves the problem that the original data cannot be directly processed and manual intervention is required in traditional eye diagram performance analysis, and the original image information analysis of the eye diagram is realized by using CNN The intelligence and automation of the oscilloscope can be used as the eye diagram software processing module of the oscilloscope and the eye diagram analysis module of the simulation software, and then embedded in the test instrument for 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...

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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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