Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel

SNR estimation has been studied extensively in the past. Nevertheless, vast majority of prior works in the design of SNR estimation algorithms are mainly focused on the assumption of Gaussian noise models. It is often assumed that the receiver noise is Gaussian distributed and arises from the receiv...

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Main Author:	Lo, Ying Siew
Format:	Thesis
Published:	2013
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
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id	my-mmu-ep.5894
record_format	uketd_dc
spelling	my-mmu-ep.58942014-12-29T03:06:46Z Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel 2013-06 Lo, Ying Siew TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television SNR estimation has been studied extensively in the past. Nevertheless, vast majority of prior works in the design of SNR estimation algorithms are mainly focused on the assumption of Gaussian noise models. It is often assumed that the receiver noise is Gaussian distributed and arises from the receiving system itself. However, the type of noise commonly encountered in practice is reported to be non Gaussian due to man-made noise and interference. As a consequence, Gaussian based SNR estimator performs poorly when the distribution of the noise deviates from Gaussian. This study aims to investigate the efficiency and robustness of the existing Gaussian-based SNR estimators when the noise distribution deviates from Gaussian and also to design an optimum SNR estimator for non-Gaussian noise channel. The two-term additive Gaussian mixture noise (AGMN) is adopted to model the non Gaussian noise. Simulation results show that the performance of existing Gaussian based SNR estimators degrades in the AGMN channel. Hence, the design of a robust SNR estimator in AGMN is necessary. 2013-06 Thesis http://shdl.mmu.edu.my/5894/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php masters Multimedia University Faculty of Engineering and Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Lo, Ying Siew Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel
description	SNR estimation has been studied extensively in the past. Nevertheless, vast majority of prior works in the design of SNR estimation algorithms are mainly focused on the assumption of Gaussian noise models. It is often assumed that the receiver noise is Gaussian distributed and arises from the receiving system itself. However, the type of noise commonly encountered in practice is reported to be non Gaussian due to man-made noise and interference. As a consequence, Gaussian based SNR estimator performs poorly when the distribution of the noise deviates from Gaussian. This study aims to investigate the efficiency and robustness of the existing Gaussian-based SNR estimators when the noise distribution deviates from Gaussian and also to design an optimum SNR estimator for non-Gaussian noise channel. The two-term additive Gaussian mixture noise (AGMN) is adopted to model the non Gaussian noise. Simulation results show that the performance of existing Gaussian based SNR estimators degrades in the AGMN channel. Hence, the design of a robust SNR estimator in AGMN is necessary.
format	Thesis
qualification_level	Master's degree
author	Lo, Ying Siew
author_facet	Lo, Ying Siew
author_sort	Lo, Ying Siew
title	Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel
title_short	Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel
title_full	Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel
title_fullStr	Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel
title_full_unstemmed	Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel
title_sort	signal-to-noise ratio (snr) estimation in additive gaussian mixture noise channel
granting_institution	Multimedia University
granting_department	Faculty of Engineering and Technology
publishDate	2013
_version_	1747829597726048256

Signal-to-Noise Ratio (SNR) estimation in additive Gaussian mixture noise channel

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