DA and NDA SINR estimation in non Gaussian noise

A Almradi; K A Hamdi

doi:10.1109/WCNC.2015.7127545

DA and NDA SINR estimation in non Gaussian noise

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

Abstract

In this paper, the problem of signal-to-interference plus noise ratio estimation over flat fading channels in non-Gaussian noise is addressed. Most previously published estimators assume the additive noise to be Gaussian. These estimation algorithms performs significantly worse when the additive noise is non-Gaussian. The additive non-Gaussian noise is modeled by a mixture of Gaussians distribution. Both data aided (DA) and non data aided (NDA) estimators are studied. The second and forth order moment based (M2M4) estimator is derived. Besides, the expectation maximization (EM) algorithm is proposed to iteratively estimate the maximum likelihood (ML) signal to interference plus noise ratio (SINR) estimate for both DA and NDA cases. The performance of the proposed estimators are compared in terms of their mean square error (MSE). Simulation results show remarkable performance improvements of the DA and NDA EM based ML SINR estimator over the M2M4 moment based estimator. Besides, it reveals the degradation in accuracy due to the impulsive noise components over Additive white Gaussian noise (AWGN).

Original language	English
Title of host publication	Wireless Communications and Networking Conference (WCNC), 2015 IEEE
Place of Publication	IEEE explore
Publisher	IEEE
Pages	642-646
Number of pages	5
DOIs	https://doi.org/10.1109/WCNC.2015.7127545
Publication status	Published - Mar 2015
Event	2015 IEEE Wireless Communications and Networking Conference (WCNC) - Duration: 9 Mar 2015 → 12 Mar 2015

Conference

Conference	2015 IEEE Wireless Communications and Networking Conference (WCNC)
Period	9/03/15 → 12/03/15

Keywords

AWGN
Gaussian distribution
expectation-maximisation algorithm
fading channels
impulse noise
DA estimation
EM algorithm
Gaussians distribution
M2M4 moment based estimator
NDA SINR estimation
additive non Gaussian noise
additive white Gaussian noise
data aided estimators
expectation maximization algorithm
flat fading channels
impulsive noise components
maximum likelihood signal-to-interference-plus-noise ratio estimation
non data aided estimators
second-and-forth order moment based estimator
signal-to-interference-plus-noise ratio estimation
Additive noise
Gaussian noise
Interference
Maximum likelihood estimation
Signal to noise ratio

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10.1109/WCNC.2015.7127545

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@inproceedings{b3beb701006c40a5bf3069c0090778c5,

title = "DA and NDA SINR estimation in non Gaussian noise",

abstract = "In this paper, the problem of signal-to-interference plus noise ratio estimation over flat fading channels in non-Gaussian noise is addressed. Most previously published estimators assume the additive noise to be Gaussian. These estimation algorithms performs significantly worse when the additive noise is non-Gaussian. The additive non-Gaussian noise is modeled by a mixture of Gaussians distribution. Both data aided (DA) and non data aided (NDA) estimators are studied. The second and forth order moment based (M2M4) estimator is derived. Besides, the expectation maximization (EM) algorithm is proposed to iteratively estimate the maximum likelihood (ML) signal to interference plus noise ratio (SINR) estimate for both DA and NDA cases. The performance of the proposed estimators are compared in terms of their mean square error (MSE). Simulation results show remarkable performance improvements of the DA and NDA EM based ML SINR estimator over the M2M4 moment based estimator. Besides, it reveals the degradation in accuracy due to the impulsive noise components over Additive white Gaussian noise (AWGN).",

keywords = "AWGN, Gaussian distribution, expectation-maximisation algorithm, fading channels, impulse noise, DA estimation, EM algorithm, Gaussians distribution, M2M4 moment based estimator, NDA SINR estimation, additive non Gaussian noise, additive white Gaussian noise, data aided estimators, expectation maximization algorithm, flat fading channels, impulsive noise components, maximum likelihood signal-to-interference-plus-noise ratio estimation, non data aided estimators, second-and-forth order moment based estimator, signal-to-interference-plus-noise ratio estimation, Additive noise, Gaussian noise, Interference, Maximum likelihood estimation, Signal to noise ratio",

author = "A Almradi and Hamdi, {K A}",

year = "2015",

month = mar,

doi = "10.1109/WCNC.2015.7127545",

language = "English",

pages = "642--646",

booktitle = "Wireless Communications and Networking Conference (WCNC), 2015 IEEE",

publisher = "IEEE",

address = "United States",

note = "2015 IEEE Wireless Communications and Networking Conference (WCNC) ; Conference date: 09-03-2015 Through 12-03-2015",

}

TY - GEN

T1 - DA and NDA SINR estimation in non Gaussian noise

AU - Almradi, A

AU - Hamdi, K A

PY - 2015/3

Y1 - 2015/3

N2 - In this paper, the problem of signal-to-interference plus noise ratio estimation over flat fading channels in non-Gaussian noise is addressed. Most previously published estimators assume the additive noise to be Gaussian. These estimation algorithms performs significantly worse when the additive noise is non-Gaussian. The additive non-Gaussian noise is modeled by a mixture of Gaussians distribution. Both data aided (DA) and non data aided (NDA) estimators are studied. The second and forth order moment based (M2M4) estimator is derived. Besides, the expectation maximization (EM) algorithm is proposed to iteratively estimate the maximum likelihood (ML) signal to interference plus noise ratio (SINR) estimate for both DA and NDA cases. The performance of the proposed estimators are compared in terms of their mean square error (MSE). Simulation results show remarkable performance improvements of the DA and NDA EM based ML SINR estimator over the M2M4 moment based estimator. Besides, it reveals the degradation in accuracy due to the impulsive noise components over Additive white Gaussian noise (AWGN).

AB - In this paper, the problem of signal-to-interference plus noise ratio estimation over flat fading channels in non-Gaussian noise is addressed. Most previously published estimators assume the additive noise to be Gaussian. These estimation algorithms performs significantly worse when the additive noise is non-Gaussian. The additive non-Gaussian noise is modeled by a mixture of Gaussians distribution. Both data aided (DA) and non data aided (NDA) estimators are studied. The second and forth order moment based (M2M4) estimator is derived. Besides, the expectation maximization (EM) algorithm is proposed to iteratively estimate the maximum likelihood (ML) signal to interference plus noise ratio (SINR) estimate for both DA and NDA cases. The performance of the proposed estimators are compared in terms of their mean square error (MSE). Simulation results show remarkable performance improvements of the DA and NDA EM based ML SINR estimator over the M2M4 moment based estimator. Besides, it reveals the degradation in accuracy due to the impulsive noise components over Additive white Gaussian noise (AWGN).

KW - AWGN

KW - Gaussian distribution

KW - expectation-maximisation algorithm

KW - fading channels

KW - impulse noise

KW - DA estimation

KW - EM algorithm

KW - Gaussians distribution

KW - M2M4 moment based estimator

KW - NDA SINR estimation

KW - additive non Gaussian noise

KW - additive white Gaussian noise

KW - data aided estimators

KW - expectation maximization algorithm

KW - flat fading channels

KW - impulsive noise components

KW - maximum likelihood signal-to-interference-plus-noise ratio estimation

KW - non data aided estimators

KW - second-and-forth order moment based estimator

KW - signal-to-interference-plus-noise ratio estimation

KW - Additive noise

KW - Gaussian noise

KW - Interference

KW - Maximum likelihood estimation

KW - Signal to noise ratio

U2 - 10.1109/WCNC.2015.7127545

DO - 10.1109/WCNC.2015.7127545

M3 - Conference contribution

SP - 642

EP - 646

BT - Wireless Communications and Networking Conference (WCNC), 2015 IEEE

PB - IEEE

CY - IEEE explore

T2 - 2015 IEEE Wireless Communications and Networking Conference (WCNC)

Y2 - 9 March 2015 through 12 March 2015

ER -

DA and NDA SINR estimation in non Gaussian noise

Abstract

Conference

Keywords

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