Dissertation/Thesis Abstract

SNR estimation and jamming detection techniques using wavelets
by Quiros, Paula Quintana, M.S., California State University, Long Beach, 2014, 125; 1569590
Abstract (Summary)

An SNR estimation approach and a jamming detector based on wavelet transform theory are presented. The SNR estimator is an in-service, non-data aided estimator that operates on M-PSK and QAM modulated signals transmitted over baseband CWGN channels. The signal and noise power are separated through a non-linear wavelet technique known as denoising.

Two wavelet-based estimators are presented. The first method uses hard thresholding which extracts the amplitude trend over one or several symbol periods, depending on whether the modulation is constant or multi-level envelope. The second method uses adaptive soft-thresholding and applies a self-similarity criterion between the signal and wavelet. A SNR Moments estimator was also developed as a reference for evaluation purposes. A jamming detector based on discontinuity recognition using wavelets is presented. The detector is implemented for constant-envelope modulation schemes, leaving the multi-level case for future research.

Indexing (document details)
Advisor: Tsang, Chit-Sang
Commitee: Mozumdar, Mohammad, Yeh, Hen-Geul
School: California State University, Long Beach
Department: Electrical Engineering
School Location: United States -- California
Source: MAI 54/01M(E), Masters Abstracts International
Subjects: Electrical engineering, Computer science
Keywords: Electrical communications, Estimation, Jamming detection, Signal processing, Signal to noise ratio, Wavelet transform
Publication Number: 1569590
ISBN: 9781321357547
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