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Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission

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arxiv 2208.08868 v1 pith:CXDEKUAG submitted 2022-07-12 eess.SP physics.optics

Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission

Yuchen Song , Danshi Wang , Qirui Fan , Xiaotian Jiang , Xiao Luo , Min Zhang This is my paper
classification eess.SP physics.optics
keywords channelfibermodellingneuraloperatoropticalphysics-informedscalable
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We propose efficient modelling of optical fiber channel via NLSE-constrained physics-informed neural operator without reference solutions. This method can be easily scalable for distance, sequence length, launch power, and signal formats, and is implemented for ultra-fast simulations of 16-QAM signal transmission with ASE noise.

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