• 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