Deep learning intelligent constellation diagram analysis method based on convolutional neural network

Abstract

The invention discloses a deep learning intelligent constellation diagram analysis method based on a convolutional neural network, and relates to the technical field of optical communication. According to the method, the performance analysis on a constellation diagram is performed by building and training a convolutional neural network module. The method comprises the following steps of acquiring a training data set of a constellation diagram; preprocessing the constellation diagram; training a CNN module for feature extraction; inputting the to-be-analyzed and preprocessed constellation diagram into the well trained CNN module for pattern recognition and performance analysis; and outputting an analysis result. According to the invention, the depth learning technology based on the convolutional neural network is applied to the constellation diagram analysis. In this way, the problem in the prior art that, the problem that original data cannot be directly processed and needs to be manually intervened during the traditional constellation diagram performance analysis can be solved. By using the CNN module, the intelligence and the automation of analysis of the original image information of the constellation diagram are achieved. As a result, the module can be used as a constellation diagram software processing and analyzing module of an oscilloscope and a constellation diagram analyzing module of the simulation software to be further embedded into a test instrument for intelligent signal analysis and performance monitoring.

Description

technical field [0001] The present invention relates to the technical field of optical communication, in particular to a deep learning intelligent constellation 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, ...

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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 constellation diagram analysis method based on convolutional neural network, and solve the problem that the original data cannot be directly processed in the traditional constellation diagram performance analysis. Disadvantages of manual intervention, realizing the intelligence and automation of performance analysis on the original image of the constellation map

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