Performance Comparison of Reconstruction Algorithms in Discrete Blind Multi-Coset Sampling

Ruben Grigoryan, Thomas Arildsen, Deepaknath Tandur, Torben Larsen

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3 Citationer (Scopus)
565 Downloads (Pure)

Abstrakt

This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals. The performance of the existing compressed sensing reconstruction algorithms have
not been investigated yet for the discrete multi-coset sampling. We compare the following algorithms – orthogonal matching pursuit, multiple signal classification, subspace-augmented multiple signal classification, focal under-determined system solver and basis pursuit denoising. The comparison is performed via numerical simulations for different sampling conditions. According to the simulations, focal under-determined system solver outperforms all other algorithms for signals with low signal-to-noise ratio. In other cases, the multiple signal classification algorithm is more beneficial.
OriginalsprogEngelsk
TitelProceedings of the 12th IEEE International Symposium on Signal Processing and Information Technology
Antal sider6
ForlagIEEE Press
Publikationsdato2012
Sider147-152
ISBN (Trykt)978-1-4673-5604-6
DOI
StatusUdgivet - 2012
BegivenhedIEEE International Symposium on Signal Processing and Information Technology - Ho Chi Minh City, Vietnam
Varighed: 12 dec. 201215 dec. 2012

Konference

KonferenceIEEE International Symposium on Signal Processing and Information Technology
LandVietnam
ByHo Chi Minh City
Periode12/12/201215/12/2012

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