Artificial Neural Networks-Assisted Geometric Shaping Optimization Including Gray-like Mapping

Xiang Liu; Xiang Liu; Jiao Zhang; Jiao Zhang; Min Zhu; Min Zhu; Bingchang Hua; Yucong Zou; Yucong Zou; Qinru Li; Yingxin Wei; Weidong Tong; Yuancheng Cai; Yuancheng Cai; Mingzheng Lei; Liang Tian; Aijie Li

doi:10.1364/ACPC.2021.T3B.5

Asia Communications and Photonics Conference 2021
Technical Digest Series (Optica Publishing Group, 2021),
paper T3B.5
•https://doi.org/10.1364/ACPC.2021.T3B.5

Artificial Neural Networks-Assisted Geometric Shaping Optimization Including Gray-like Mapping

Xiang Liu, Jiao Zhang, Min Zhu, Bingchang Hua, Yucong Zou, Qinru Li, Yingxin Wei, Weidong Tong, Yuancheng Cai, Mingzheng Lei, Liang Tian, and Aijie Li

Asia Communications and Photonics Conference 2021

Shanghai China
24–27 October 2021
ISBN: 978-1-957171-00-5

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X. Liu, J. Zhang, M. Zhu, B. Hua, Y. Zou, Q. Li, Y. Wei, W. Tong, Y. Cai, M. Lei, L. Tian, and A. Li, "Artificial Neural Networks-Assisted Geometric Shaping Optimization Including Gray-like Mapping," in Asia Communications and Photonics Conference 2021, C. Chang-Hasnain, A. Willner, W. Shieh, P. Shum, Y. Su, G. Li, B. Eggleton, R. Essiambre, D. Dai, and D. Ma, eds., Technical Digest Series (Optica Publishing Group, 2021), paper T3B.5.

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Abstract

Artificial Neural networks-based geometric shaping is proposed that includes Gray-like mappings. Over 0.2dB gain in GMI and BER improvement is achieved over a wide range of SNRs without requiring any presumed model for the channel.

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