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

作者： Yuchen Song Danshi Wang Qirui Fan Xiaotian Jiang Xiao Luo Min Zhang
提交时间：2023-02-19 19:58:59

摘要: 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.

分类： 光学 >> 量子光学
引用： ChinaXiv:202303.00980 (或此版本 ChinaXiv:202303.00980V1)
DOI:10.12074/202303.00980V1
CSTR:32003.36.ChinaXiv.202303.00980.V1
科创链TXID： 3138b6ea-4d65-4450-b396-2b83705d565d
推荐引用方式： Yuchen Song,Danshi Wang,Qirui Fan,Xiaotian Jiang,Xiao Luo,Min Zhang.Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission.中国科学院科技论文预发布平台.[ChinaXiv:202303.00980V1] (点此复制)

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2023-02-19 19:58:59

ChinaXiv:202303.00980V1

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

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