Parameter Estimation of Bernoulli-Gaussian Noise Using Fractional Order Absolute Moments

Xiaoxiao Liu; Jing Xu; Yingwen Liu

doi:10.1109/LCOMM.2022.3211067

Parameter Estimation of Bernoulli-Gaussian Noise Using Fractional Order Absolute Moments

Xiaoxiao Liu
, Jing Xu^*
, Yingwen Liu

^*Corresponding author for this work

School of Communication and Electronic Engineering

East China Normal University

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

The prior information of impulsive noise at receiver will improve the performance of the communication systems impaired by impulsive noise. Therefore, parameters estimation techniques of Bernoulli-Gaussian noise are studied in this letter. A Fractional Order absolute Moment-based Estimator (FOME) is proposed and the Asymptotic Normalized Mean Square Error (ANMSE) of the FOME is derived. Both the ANMSE and simulation results show that the FOME performs better than the 1,2,3-order moment-based estimator, and the performance of the FOME will approach the expectation maximization estimator with the increase of the received signal length.

Original language	English
Pages (from-to)	312-316
Number of pages	5
Journal	IEEE Communications Letters
Volume	27
Issue number	1
DOIs	https://doi.org/10.1109/LCOMM.2022.3211067
State	Published - 1 Jan 2023

Keywords

Bernoulli-Gaussian noise
Impulsive noise
moment-based estimator

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10.1109/LCOMM.2022.3211067

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title = "Parameter Estimation of Bernoulli-Gaussian Noise Using Fractional Order Absolute Moments",

abstract = "The prior information of impulsive noise at receiver will improve the performance of the communication systems impaired by impulsive noise. Therefore, parameters estimation techniques of Bernoulli-Gaussian noise are studied in this letter. A Fractional Order absolute Moment-based Estimator (FOME) is proposed and the Asymptotic Normalized Mean Square Error (ANMSE) of the FOME is derived. Both the ANMSE and simulation results show that the FOME performs better than the 1,2,3-order moment-based estimator, and the performance of the FOME will approach the expectation maximization estimator with the increase of the received signal length.",

keywords = "Bernoulli-Gaussian noise, Impulsive noise, moment-based estimator",

author = "Xiaoxiao Liu and Jing Xu and Yingwen Liu",

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AU - Liu, Xiaoxiao

AU - Xu, Jing

AU - Liu, Yingwen

PY - 2023/1/1

Y1 - 2023/1/1

N2 - The prior information of impulsive noise at receiver will improve the performance of the communication systems impaired by impulsive noise. Therefore, parameters estimation techniques of Bernoulli-Gaussian noise are studied in this letter. A Fractional Order absolute Moment-based Estimator (FOME) is proposed and the Asymptotic Normalized Mean Square Error (ANMSE) of the FOME is derived. Both the ANMSE and simulation results show that the FOME performs better than the 1,2,3-order moment-based estimator, and the performance of the FOME will approach the expectation maximization estimator with the increase of the received signal length.

AB - The prior information of impulsive noise at receiver will improve the performance of the communication systems impaired by impulsive noise. Therefore, parameters estimation techniques of Bernoulli-Gaussian noise are studied in this letter. A Fractional Order absolute Moment-based Estimator (FOME) is proposed and the Asymptotic Normalized Mean Square Error (ANMSE) of the FOME is derived. Both the ANMSE and simulation results show that the FOME performs better than the 1,2,3-order moment-based estimator, and the performance of the FOME will approach the expectation maximization estimator with the increase of the received signal length.

KW - Bernoulli-Gaussian noise

KW - Impulsive noise

KW - moment-based estimator

UR - https://www.scopus.com/pages/publications/85139521695

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DO - 10.1109/LCOMM.2022.3211067

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SN - 1089-7798

VL - 27

SP - 312

EP - 316

JO - IEEE Communications Letters

JF - IEEE Communications Letters

IS - 1

ER -

Parameter Estimation of Bernoulli-Gaussian Noise Using Fractional Order Absolute Moments

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Keywords

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