DocumentCode :
3084196
Title :
Multi-coset sampling for power spectrum blind sensing
Author :
Ariananda, Dyonisius Dony ; Leus, Geert ; Tian, Zhi
Author_Institution :
Fac. of EEMCS, Delft Univ. of Technol., Delft, Netherlands
fYear :
2011
fDate :
6-8 July 2011
Firstpage :
1
Lastpage :
8
Abstract :
Power spectrum blind sampling (PSBS) consists of a sampling procedure and a reconstruction method that is able to recover the unknown power spectrum of a random signal from the obtained sub-Nyquist-rate samples. It differs from spectrum blind sampling (SBS) that aims to recover the spectrum instead of the power spectrum of the signal. In this paper, a PSBS solution is first presented based on a periodic sampling procedure. Then, a multi-coset implementation for this sampling procedure is developed by solving the so-called minimal sparse ruler problem, and the coprime sampling technique is tailored to fit into the PSBS framework as well. It is shown that the proposed multi-coset implementation based on minimal sparse rulers offers advantages over coprime sampling in terms of reduced sampling rates, increased flexibility and an extended range of estimated auto-correlation lags. These benefits arise without putting any sparsity constraint on the power spectrum. Application to sparse power spectrum recovery is also illustrated.
Keywords :
correlation methods; signal reconstruction; signal sampling; auto-correlation lags; coprime sampling technique; minimal sparse ruler problem; multicoset sampling; power spectrum blind sensing; random signal; reconstruction method; sparsity constraint; sub-Nyquist-rate samples; Artificial neural networks; Indexes; Multi-coset sampling; coprime sampling; sparse ruler;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Signal Processing (DSP), 2011 17th International Conference on
Conference_Location :
Corfu
ISSN :
Pending
Print_ISBN :
978-1-4577-0273-0
Type :
conf
DOI :
10.1109/ICDSP.2011.6005003
Filename :
6005003
Link To Document :
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