Dynamic Joint Frequency Offset and Phase Noise Tracking by Number-Theoretic Net-Based Gaussian Particle Filter in Coherent Optical Systems

Xinwei Du, Yangfan Xu, Wei Huang, Changyuan Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

A number-theoretic net (NT-net)-based Gaussian particle filter (NT-GPF) is proposed for the joint estimation of frequency offset (FO) and linear phase noise (LPN) in a dynamic and real-time manner. Based on the concept of uniform design, the NT-net can uniformly generate particles on an ellipse for a given bivariate normal distribution. As a result, the NT-GPF can achieve accurate FO and LPN tracking with only a small number of particles, which greatly improves the estimation efficiency and noise tolerance compared with the conventional GPF. We also propose a recursive signal detection algorithm to further enhance the final system bit error rate (BER) performance. Simulations verify the accuracy, robustness and efficiency of the NT-GPF.

Original languageEnglish
Pages (from-to)1388-1392
Number of pages5
JournalIEEE Communications Letters
Volume26
Issue number6
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • frequency offset
  • linear phase noise
  • Number-theoretic net
  • particle filter

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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