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Reconstruction of Complex Sparse Signals in Compressed Sensing with Real Sensing Matrices

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Abstract

The existing greedy algorithms for the reconstruction in compressed sensing were designed no matter which type the original sparse signals and sensing matrices have, real or complex. The reconstruction algorithms definitely apply to real sensing matrices and complex sparse signals, but they are not customized to this situation so that we could improve those algorithms further. In this paper, we elaborate on the compressed sensing with real sensing matrices when the original sparse signals are complex. We propose two reconstruction algorithms by modifying the orthogonal matching pursuit to include some procedures specialized to this setting. It is shown via analysis and simulation that the proposed algorithms have better reconstruction success probability than conventional reconstruction algorithms.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01060941) and the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2014-0-00636) supervised by the IITP (Institute for Information & communications Technology Promotion).

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Authors and Affiliations

  1. School of Electronics and Computer Engineering, Chonnam National University, Gwangju, 61186, Korea

    Hosung Park

  2. Department of Electronic Engineering, Soonchunhyang University, Asan, 31538, Korea

    Kee-Hoon Kim

  3. Department of Electrical and Computer Engineering, INMC, Seoul National University, Seoul, 08826, Korea

    Jong-Seon No

  4. Department of Information and Communication Engineering, Dongguk University-Seoul, Seoul, 100-715, Korea

    Dae-Woon Lim

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  1. Hosung Park
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  2. Kee-Hoon Kim
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Correspondence to Dae-Woon Lim.

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Park, H., Kim, KH., No, JS. et al. Reconstruction of Complex Sparse Signals in Compressed Sensing with Real Sensing Matrices. Wireless Pers Commun 97, 5719–5731 (2017). https://doi.org/10.1007/s11277-017-4805-z

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  • DOI: https://doi.org/10.1007/s11277-017-4805-z

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