Structured Sparsity Learning-based pruned retraining Volterra Equalization for Data-Center Interconnects

Govind Sharan Yadav; Chun-Yen Chuang; Kai-Ming Feng; Kai-Ming Feng; Jyehong Chen; Young-Kai Chen

doi:10.1364/OFC.2021.W6A.23

Optical Fiber Communication Conference (OFC) 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper W6A.23
•https://doi.org/10.1364/OFC.2021.W6A.23

Structured Sparsity Learning-based pruned retraining Volterra Equalization for Data-Center Interconnects

Govind Sharan Yadav, Chun-Yen Chuang, Kai-Ming Feng, Jyehong Chen, and Young-Kai Chen

Optical Fiber Communication Conference 2021

Washington, DC United States
6–11 June 2021
ISBN: 978-1-943580-86-6

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Joint Poster Session 1 (W6A)

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G. S. Yadav, C. Chuang, K. Feng, J. Chen, and Y. Chen, "Structured Sparsity Learning-based pruned retraining Volterra Equalization for Data-Center Interconnects," in Optical Fiber Communication Conference (OFC) 2021, P. Dong, J. Kani, C. Xie, R. Casellas, C. Cole, and M. Li, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper W6A.23.

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Abstract

We propose a structured sparsity learning-based pruned retraining Volterra equalization for inter-dadta-center interconnects. Compared with conventional VE, we achieve 95% and 90.5% complexity reduction without signal degradation for B2B and 40-km at 80-Gb/s PAM4, respectively.

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