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Pruned Volterra Nonlinear Equalization using Sparse Bayesian Learning and Kalman Filtering

  • Hualei Shi
  • , Feng Tian*
  • , Chuanji Yan
  • , Yutian Li
  • , Qi Zhang
  • , Ran Gao
  • , Qinghua Tian
  • , Fu Wang
  • , Zhipei Li
  • *Corresponding author for this work
  • State Key Laboratory of Information Photonics and Optical Communications
  • Beijing University of Posts and Telecommunications
  • Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A sparsified Volterra algorithm based on sparse Bayesian learning and Kalman filtering is proposed and experimentally demonstrated in a 20G-baud PAM4 IM/DD system, reducing the average retained kernel number by 90.43%.

Original languageEnglish
Title of host publication2026 24th International Conference on Optical Communications and Networks, ICOCN 2026
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798319508171
DOIs
Publication statusPublished - 2026
Externally publishedYes
Event24th International Conference on Optical Communications and Networks, ICOCN 2026 - Xining, China
Duration: 20 Jul 2026 → 23 Jul 2026

Publication series

Name2026 24th International Conference on Optical Communications and Networks, ICOCN 2026

Conference

Conference24th International Conference on Optical Communications and Networks, ICOCN 2026
Country/TerritoryChina
CityXining
Period20/07/26 → 23/07/26

Keywords

  • IM/DD optical communication system
  • Kalman filtering
  • Sparse Bayesian learning
  • Volterra

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