Number-Theoretic Net-Based Particle Filtering for Linear Phase Noise Tracking in CO-OFDM Systems

Yangfan Xu, Xinwei Du, Shuai Liu, Changyuan Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a number-theoretic net-based particle filter (NT- PF) to dynamically estimate the linear phase noise (LPN) for CO-OFDM systems in the time domain. Inspired by the concept of uniform design (UD), we recursively draw the particles from the NT -net at each time index, which leads to a significant improvement on the algorithm efficiency compared with the conventional Gaussian particle filter (GPF). In addition, we propose a signal detection approach to estimate the LPN on the entire received signal first, then recover the transmitted signal from the received ones. The dynamic tracking performance, efficiency and robustness of the proposed NT-PF is verified in an 89 Gb/s 16-QAM CO-OFDM system.

Original languageEnglish
Title of host publicationICOCN 2022 - 20th International Conference on Optical Communications and Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
ISBN (Electronic)9781665458986
DOIs
Publication statusPublished - Aug 2022
Event20th International Conference on Optical Communications and Networks, ICOCN 2022 - Shenzhen, China
Duration: 12 Aug 202215 Aug 2022

Publication series

NameICOCN 2022 - 20th International Conference on Optical Communications and Networks

Conference

Conference20th International Conference on Optical Communications and Networks, ICOCN 2022
Country/TerritoryChina
CityShenzhen
Period12/08/2215/08/22

Keywords

  • CO-OFDM
  • linear phase noise
  • Number-theoretic net
  • particle filter
  • uniform design

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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