DocumentCode
2090550
Title
Analog compressed sensing for multiband signals with non-modulated Slepian basis
Author
Xianjun Yang ; Dutkiewicz, Eryk ; Qimei Cui ; Xiaojing Huang ; Xiaofeng Tao ; Gengfa Fang
Author_Institution
Dept. of Electron. Eng., Macquarie Univ., Sydney, NSW, Australia
fYear
2013
fDate
9-13 June 2013
Firstpage
4941
Lastpage
4945
Abstract
Recently, the recovery performance of analog Compressed Sensing (CS) has been significantly improved by representing multiband signals with the modulated and merged Slepian basis (MM-Slepian dictionary), which avoids the frequency leakage effect of the Discrete Fourier Transform (DFT) basis. However, the MM-Slepian dictionary has a very large scale and corresponds to a large-scale measurement matrix, which leads to high recovery computational complexity. This paper resolves the above problem by modulating and band-limiting the multiband signal rather than modulating the Slepian basis. Specifically, instead of using the MM-Slepian dictionary to represent the whole multiband signal, we propose to use the non-modulated Slepian basis to represent the modulated and band-limited version of the multiband signal based on the recently proposed Modulated Wideband Converter (MWC). Furthermore, based on the analytical derivation with the non-modulated Slepian basis, we propose an Interpolation Recovery (IR) algorithm to take full advantage of the Slepian basis, whereas the Direct Recovery (DR) algorithm using the Moore-Penrose pseudo-inverse cannot achieve this. Simulation results verify that, with low recovery computational load, the non-modulated Slepian basis combined with the IR algorithm improves the recovery SNR by up to 35 dB compared with the DFT basis in noise-free environment.
Keywords
compressed sensing; computational complexity; discrete Fourier transforms; interpolation; matrix algebra; CS; DFT basis; DR algorithm; IR algorithm; MM-Slepian dictionary; MWC; Moore-Penrose pseudo-inverse; analog compressed sensing; analytical derivation; computational complexity; direct recovery algorithm; discrete Fourier transform basis; frequency leakage effect avoidance; interpolation recovery algorithm; large-scale measurement matrix; low recovery computational load; modulated wideband converter; multiband signals; noise-free environment; nonmodulated Slepian basis; Algorithm design and analysis; Compressed sensing; Dictionaries; Discrete Fourier transforms; Estimation; Signal to noise ratio; Wideband; Analog Compressed Sensing; Multiband Signal; Slepian Basis;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications (ICC), 2013 IEEE International Conference on
Conference_Location
Budapest
ISSN
1550-3607
Type
conf
DOI
10.1109/ICC.2013.6655361
Filename
6655361
Link To Document