Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICC.2013.6655361 | ICC |
Keywords | Field | DocType |
multiband signals,interpolation recovery algorithm,multiband signal,interpolation,modulated wideband converter,matrix algebra,analog compressed sensing,dr algorithm,moore-penrose pseudo-inverse,mwc,ir algorithm,noise-free environment,frequency leakage effect avoidance,dft basis,computational complexity,discrete fourier transforms,mm-slepian dictionary,compressed sensing,analytical derivation,low recovery computational load,nonmodulated slepian basis,cs,slepian basis,direct recovery algorithm,large-scale measurement matrix,discrete fourier transform basis,estimation,algorithm design and analysis,signal to noise ratio,dictionaries | Wideband,Computer science,Matrix (mathematics),Matrix algebra,Interpolation,Algorithm,Theoretical computer science,Real-time computing,Discrete Fourier transform,Compressed sensing,Computational complexity theory | Conference |
Volume | Issue | ISSN |
null | null | 1550-3607 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianjun Yang | 1 | 70 | 4.38 |
| Eryk Dutkiewicz | 2 | 891 | 122.78 |
| Qimei Cui | 3 | 642 | 79.84 |
| Xiaojing Huang | 4 | 61 | 10.16 |
| Xiaofeng Tao | 5 | 1033 | 140.26 |
| Gengfa Fang | 6 | 128 | 24.24 |