DocumentCode :
2033396
Title :
Power spectrum blind sampling using minimum mean square error and weighted least squares
Author :
Tausiesakul, Bamrung ; Gonzalez-Prelcic, Nuria
Author_Institution :
E.E. Telecomun., Univ. de Vigo, Vigo, Spain
fYear :
2013
fDate :
3-6 Nov. 2013
Firstpage :
153
Lastpage :
157
Abstract :
We present a new power spectrum recovery method in the context of power spectrum blind sampling. As sampling device we propose a multicoset sampler, which provides sub-Nyquist rate samples. A weighted least squares (WLS) criterion is adopted with the aim to define a power spectrum recovery algorithm that minimizes the mean square error (MSE) of the correlation estimate of the input signal. It is analytically shown that the optimal weighting matrix is equal to the inverse of the covariance matrix of the correlation estimate of the sub-Nyquist rate samples. The derived weight can also be shown to be optimal in MSE sense for power spectrum estimation. We also provide an optimization framework for the design of multicoset sampling patterns that minimize the MSE of the compressive WLS power spectrum estimator. The resulting integer nonlinear programming problem is solved by using exhaustive search.
Keywords :
compressed sensing; correlation methods; estimation theory; least squares approximations; mean square error methods; signal reconstruction; signal sampling; MSE; WLS criterion; compressive WLS power spectrum estimator; correlation estimate; covariance matrix; exhaustive search; integer nonlinear programming problem; mean square error; multicoset sampler; multicoset sampling patterns; optimal weighting matrix; optimization framework; power spectrum blind sampling; power spectrum estimation; power spectrum recovery method; subNyquist rate samples; weighted least squares criterion; Cognitive radio; Correlation; Least squares approximations; Mean square error methods; Optimization; Spectral analysis; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Systems and Computers, 2013 Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4799-2388-5
Type :
conf
DOI :
10.1109/ACSSC.2013.6810249
Filename :
6810249
Link To Document :
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