Optical performance monitoring method, electronic equipment and computer readable storage medium

Abstract

The invention provides an optical performance monitoring method, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the preprocessing of a received optical signal, and obtaining a complex signal; generating a constellation diagram based on the complex signal; converting the constellation diagram into a grey-scale map, and extracting a grey-scale value from the grey-scale map; converting the extracted gray value into a complex value by using a preset conversion algorithm; and inputting the complex value into a pre-established complex value neural network to obtain a modulation format and / or an optical signal-to-noise ratio of the optical signal. The modulation format identification and / or the estimation of the optical signal-to-noise ratio are / is realized by utilizing the complex valued neural network, and the optical signal data are processed by adopting a picture processing mode similar to a neural network, so the processed data can be applied to the complex valued neural network, more feature information of the original data can be reserved, the optical performance monitoring precision is high, and the cost is low.

Description

technical field [0001] The present application relates to the technical field of optical communication, and in particular to an optical performance monitoring method, electronic equipment, and a computer-readable storage medium. Background technique [0002] Optical performance monitoring is an important part of an optical network system. The development of optical performance monitoring is crucial to the development of digital signal processing technology. In coherent optical communication, it is very important to choose a superior modulation format identification (ModulationFormat Identification, MFI) and optical signal to noise ratio (Optical Signal To Noise Ratio, OSNR) estimation method. Several better schemes have been proposed so far, for example, using the amplitude histogram information of the signal as an input deep neural network to realize modulation format identification and optical signal-to-noise ratio monitoring, but this scheme sacrifices complexity for mon...

AI Technical Summary

Problems solved by technology

Several better schemes have been proposed so far, for example, using the amplitude histogram information of the signal as an input deep neural network to realize modulation format identification and optical signal-to-noise ratio monitoring, but this scheme sacrifices complexity for monitoring effectiveness, The artificial neural network or support vector machine based on the cumulative distribution function is used to realize modulation format recognition and optical signal-to-noise ratio monitoring, but these schemes show certain limitations in massive data processing and anti-noise performance

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