Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals

Spectrum monitoring is important, not only to regulatory bodies for spectrum management, but also to the military for intelligence gathering. In recent years, it has become part of spectrum sensing process which is the key in cognitive radio system. Among the features of a spectrum monitoring system...

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Main Author:	Chee, Yen Mei
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/35820/5/CheeYenMeiPFKE2013.pdf
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spelling	my-utm-ep.358202017-07-18T06:54:55Z Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals 2013-05 Chee, Yen Mei TK Electrical engineering. Electronics Nuclear engineering Spectrum monitoring is important, not only to regulatory bodies for spectrum management, but also to the military for intelligence gathering. In recent years, it has become part of spectrum sensing process which is the key in cognitive radio system. Among the features of a spectrum monitoring system is to obtain spectrum usage characteristics and determining signal modulation parameters. All these required a powerful signal analysis technique suitable for use with classifier network. The loss of phase information in the Quadratic Time–Frequency Distributions (QTFDs) makes it an incomplete solution as Phase Shift Keying (PSK) modulation is widely employed in many wireless communication applications nowadays. Therefore, Cross Time–Frequency Distribution (XTFD) which can provide localised phase information is proposed in this research. The Adaptive Windowed Cross Wigner– Ville Distribution (AW–XWVD) and Adaptive Smoothed Windowed Cross Wigner– Ville Distribution (ASW–XWVD) are developed to analyse a broader class of signals such as PSK, Quadrature Amplitude Modulation (QAM), Amplitude Shift Keying (ASK) and Frequency Shift Keying (FSK) signals without any prior knowledge. In non–cooperative environment, two kernel adaptation methods are proposed: local and global adaptive. The developed XTFD is proven to be an efficient estimator as it meets the Cramer–Rao Lower Bound (CRLB) for phase estimation at Signal-to- Noise Ratio (SNR) =4 dB and Instantaneous Frequency (IF) estimation at SNR =–3 dB. Other TFDs such as the S–transform never meet the CRLB in both phase and frequency estimation. A complete signal analysis and classification system is implemented by combining the AW–XWVD and ASW–XWVD for signal analysis. In the presence of Additive White Gaussian Noise, the classifier gives 90% correct classification for all the signals at SNR of about 6 dB. Thus, it has been demonstrated that the XTFD is a complete solution for the analysis and classification of digitally modulated signals. 2013-05 Thesis http://eprints.utm.my/id/eprint/35820/ http://eprints.utm.my/id/eprint/35820/5/CheeYenMeiPFKE2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70029?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Chee, Yen Mei Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals
description	Spectrum monitoring is important, not only to regulatory bodies for spectrum management, but also to the military for intelligence gathering. In recent years, it has become part of spectrum sensing process which is the key in cognitive radio system. Among the features of a spectrum monitoring system is to obtain spectrum usage characteristics and determining signal modulation parameters. All these required a powerful signal analysis technique suitable for use with classifier network. The loss of phase information in the Quadratic Time–Frequency Distributions (QTFDs) makes it an incomplete solution as Phase Shift Keying (PSK) modulation is widely employed in many wireless communication applications nowadays. Therefore, Cross Time–Frequency Distribution (XTFD) which can provide localised phase information is proposed in this research. The Adaptive Windowed Cross Wigner– Ville Distribution (AW–XWVD) and Adaptive Smoothed Windowed Cross Wigner– Ville Distribution (ASW–XWVD) are developed to analyse a broader class of signals such as PSK, Quadrature Amplitude Modulation (QAM), Amplitude Shift Keying (ASK) and Frequency Shift Keying (FSK) signals without any prior knowledge. In non–cooperative environment, two kernel adaptation methods are proposed: local and global adaptive. The developed XTFD is proven to be an efficient estimator as it meets the Cramer–Rao Lower Bound (CRLB) for phase estimation at Signal-to- Noise Ratio (SNR) =4 dB and Instantaneous Frequency (IF) estimation at SNR =–3 dB. Other TFDs such as the S–transform never meet the CRLB in both phase and frequency estimation. A complete signal analysis and classification system is implemented by combining the AW–XWVD and ASW–XWVD for signal analysis. In the presence of Additive White Gaussian Noise, the classifier gives 90% correct classification for all the signals at SNR of about 6 dB. Thus, it has been demonstrated that the XTFD is a complete solution for the analysis and classification of digitally modulated signals.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Chee, Yen Mei
author_facet	Chee, Yen Mei
author_sort	Chee, Yen Mei
title	Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals
title_short	Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals
title_full	Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals
title_fullStr	Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals
title_full_unstemmed	Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals
title_sort	adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/35820/5/CheeYenMeiPFKE2013.pdf
_version_	1747816363681906688

Adaptive cross wigner-ville distribution for parameter estimation of digitally modulated signals

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