Noisy samples-robust Neural Network Equalizer for Coherent Optical Transceiver Nonlinearity Compensation

Zicai Cao; Shuchang Yao; Longquan Dai; Ziheng Zhang; Jing Dai; Ming Luo; Xi Xiao; Xi Xiao; Yaqin Wang; Qi Yang; Deming Liu; Lei Deng

doi:10.1364/OFC.2023.W2B.14

Optical Fiber Communication Conference (OFC) 2023
Technical Digest Series (Optica Publishing Group, 2023),
paper W2B.14
•https://doi.org/10.1364/OFC.2023.W2B.14

Noisy samples-robust Neural Network Equalizer for Coherent Optical Transceiver Nonlinearity Compensation

Zicai Cao, Shuchang Yao, Longquan Dai, Ziheng Zhang, Jing Dai, Ming Luo, Xi Xiao, Yaqin Wang, Qi Yang, Deming Liu, and Lei Deng

Optical Fiber Communication Conference 2023

San Diego California United States
5–9 March 2023
ISBN: 978-1-957171-18-0

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Posters Session II, Remote (W2B)

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Z. Cao, S. Yao, L. Dai, Z. Zhang, J. Dai, M. Luo, X. Xiao, Y. Wang, Q. Yang, D. Liu, and L. Deng, "Noisy samples-robust Neural Network Equalizer for Coherent Optical Transceiver Nonlinearity Compensation," in Optical Fiber Communication Conference (OFC) 2023, Technical Digest Series (Optica Publishing Group, 2023), paper W2B.14.

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Abstract

We experimentally demonstrate a neural network equalizer with robustness for noisy samples on a silicon photonics coherent transceiver, and a complexity reduction is over 50% at the BER of 1.25e-2 compared with a general NNLE.

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