复杂全连接神经网络在偏振分复用光纤通信线路非线性补偿中的应用

IF 0.9 4区工程技术 Q3 Engineering

Quantum Electronics Pub Date : 2021-12-01 DOI:10.1070/qel17656

S. Bogdanov, O. Sidelnikov, A. Redyuk

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引用次数: 1

摘要

提出了一种基于复值算法的全连接神经网络的极化分复用扩展光纤通信线路非线性失真补偿方案。所开发的方案的激活函数使得考虑来自不同极化分量的信号的非线性相互作用成为可能。将该方案与线性方案和独立处理不同极化信号的神经网络进行了比较，证明了所提出的神经网络结构的优越性。

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Application of complex fully connected neural networks to compensate for nonlinearity in fibre-optic communication lines with polarisation division multiplexing

A scheme is proposed to compensate for nonlinear distortions in extended fibre-optic communication lines with polarisation division multiplexing, based on fully connected neural networks with complex-valued arithmetic. The activation function of the developed scheme makes it possible to take into account the nonlinear interaction of signals from different polarisation components. This scheme is compared with a linear one and a neural network that processes signals of different polarisations independently, and the superiority of the proposed neural network architecture is demonstrated.

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来源期刊

Quantum Electronics 工程技术-工程：电子与电气

CiteScore

3.00

自引率

11.10%

发文量

审稿时长

3-6 weeks

期刊介绍： Quantum Electronics covers the following principal headings Letters Lasers Active Media Interaction of Laser Radiation with Matter Laser Plasma Nonlinear Optical Phenomena Nanotechnologies Quantum Electronic Devices Optical Processing of Information Fiber and Integrated Optics Laser Applications in Technology and Metrology, Biology and Medicine.