Implicit triple neural network and optical fiber nonlinear damage equalization method

A nonlinear damage and neural network technology, applied in the field of implicit triplet neural network and optical fiber nonlinear damage equalization, can solve the problems of large training data and high complexity

Active Publication Date: 2020-11-10
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0004] The purpose of the present invention is to propose an implicit triplet neural network and an optical fiber nonlinear damage equalization method i

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  • Implicit triple neural network and optical fiber nonlinear damage equalization method
  • Implicit triple neural network and optical fiber nonlinear damage equalization method
  • Implicit triple neural network and optical fiber nonlinear damage equalization method

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Embodiment 1

[0075] Each channel generates two strings of pseudo-random bit sequences (PRBS) for two polarization state transmissions, to attach figure 1 The mapping relationship of the two strings of PRBS is mapped to the constellation diagram to obtain a dual-polarization 16QAM symbol as a label symbol stream. According to attached figure 2 The system schematic diagram to build a simulation system, and change the fiber input power and transmission distance (the fiber input power is from -4dBm to 2dBm per channel, the step is 1; the transmission distance is from 2400km to 4000km, the step is 80km), and generate different data set under the condition. The size of the training set ranges from 3000 symbols to 32768 symbols, and the size of both validation and test sets is 32768 symbols.

[0076] According to attached image 3 The structural diagram of a constructing the implicit triplet neural network to be optimized. For data sets under different conditions, attached image 3 The proc...

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Abstract

The invention relates to an implicit triad neural network and an optical fiber nonlinear damage equalization method, and belongs to the technical field of optical fiber communication and equalization.The neural network comprises an implicit triad neural network and an optical fiber nonlinear damage equalization method based on the implicit triad neural network. The method comprises the followingsteps: 1) generating a training set, a verification set and a test set, specifically, generating a binary bit stream, generating a label symbol stream, obtaining a symbol stream to be processed, generating a sample and dividing a data set; 2) optimizing an implicit triad neural network to obtain an optimal implicit triad neural network, initializing a hyper-parameter search process, initializing an adjustable parameter iteration process, calculating a loss function and gradients of all adjustable parameters, updating the adjustable parameters, iterating and evaluating an optimization result, and selecting an optimal implicit triple neural network; 3) testing the implicit triple neural network to obtain an equalized signal. Compared with the prior art, the neural network and the method arelower in calculation cost, and the equalization effect can be further improved.

Description

technical field [0001] The invention relates to an implicit triple group neural network and an optical fiber nonlinear damage equalization method, belonging to the technical field of optical fiber communication and equalization. Background technique [0002] The capacity of fiber optic communication systems is limited by both linear and nonlinear impairments of optical fibers. With the development of optical fiber communication technology, the communication capacity of optical fiber communication system has approached the Shannon limit in the linear region. To further improve the communication capacity of the optical fiber communication system needs to break through the limitation of optical fiber nonlinear damage. Typical optical fiber nonlinear compensation methods include digital signal processing compensation methods in addition to link optical compensation methods. Compensation methods for digital signal processing include digital back-propagation (DBP, digital back-p...

Claims

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Application Information

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IPC IPC(8): H04B10/2543H04B10/516H04B10/61G06N3/04G06N3/08
CPCH04B10/2543H04B10/516H04B10/6163G06N3/08G06N3/045
Inventor 杨爱英何品靖郭芃冯立辉忻向军
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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