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

doi:10.1109/LCOMM.2022.3158744

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

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

8 Citations (Scopus)

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 language	English
Pages (from-to)	1388-1392
Number of pages	5
Journal	IEEE Communications Letters
Volume	26
Issue number	6
DOIs	https://doi.org/10.1109/LCOMM.2022.3158744
Publication status	Published - 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

More information

10.1109/LCOMM.2022.3158744

Cite this

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

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.",

keywords = "frequency offset, linear phase noise, Number-theoretic net, particle filter",

author = "Xinwei Du and Yangfan Xu and Wei Huang and Changyuan Yu",

note = "The authors would like to gratefully acknowledge the financial support of Grant R72021107 from UIC, Grant UICR0400009-21 from Guangdong College Enhancement and Innovation Program, Grant 61971372 from NSFC, and HK RGC GRF 15200718. Publisher Copyright: {\textcopyright} 1997-2012 IEEE.",

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doi = "10.1109/LCOMM.2022.3158744",

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AU - Du, Xinwei

AU - Xu, Yangfan

AU - Huang, Wei

AU - Yu, Changyuan

N1 - The authors would like to gratefully acknowledge the financial support of Grant R72021107 from UIC, Grant UICR0400009-21 from Guangdong College Enhancement and Innovation Program, Grant 61971372 from NSFC, and HK RGC GRF 15200718. Publisher Copyright: © 1997-2012 IEEE.

PY - 2022/6/1

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N2 - 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.

AB - 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.

KW - frequency offset

KW - linear phase noise

KW - Number-theoretic net

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

Abstract

Keywords

ASJC Scopus subject areas

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