Low-Complexity Multi-symbol Output Complex-Valued Neural Network for Nonlinear Equalization in 100G Coherent Photonic- assisted W-band Fiber-wireless Integrated Communication

Qijun Bian; Junlian Jia; Zhongya Li; Jianyang Shi; Nan Chi; Nan Chi; Junwen Zhang; Junwen Zhang

European Conference on Optical Communication (ECOC) 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper We5.53

Low-Complexity Multi-symbol Output Complex-Valued Neural Network for Nonlinear Equalization in 100G Coherent Photonic- assisted W-band Fiber-wireless Integrated Communication

Qijun Bian, Junlian Jia, Zhongya Li, Jianyang Shi, Nan Chi, and Junwen Zhang

European Conference and Exhibition on Optical Communication 2022

Basel Switzerland
18–22 September 2022
ISBN: 978-1-957171-15-9

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Q. Bian, J. Jia, Z. Li, J. Shi, N. Chi, and J. Zhang, "Low-Complexity Multi-symbol Output Complex-Valued Neural Network for Nonlinear Equalization in 100G Coherent Photonic- assisted W-band Fiber-wireless Integrated Communication," in European Conference on Optical Communication (ECOC) 2022, J. Leuthold, C. Harder, B. Offrein, and H. Limberger, eds., Technical Digest Series (Optica Publishing Group, 2022), paper We5.53.

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Abstract

A low-complexity multi-symbol complex-valued NN nonlinear-equalizer is proposed and experimentally demonstrated in coherent photonics-assisted MMW communication. Effective nonlinear compensation is demonstrated for 100Gbps 16-QAM photonic-assisted W-band signal after fiber-wireless integrated transmission, while the computational complexity is reduced by up to 78.1% compared with single-output NN.

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