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Feedback acquisition and reconstruction of spectrum-sparse signals by predictive level comparisons

Boloursaz Mashhad, M ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1109/LSP.2018.2801836
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2018
  4. Abstract:
  5. In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based compressive sensing algorithms, our proposed algorithm gradually estimates and follows slow changes in the sparse components utilizing a sliding-window technique. We also consider the scenario in which possible flipping errors in the sign bits propagate along iterations (due to the feedback loop) during reconstruction. We propose an iterative error correction algorithm to cope with this error propagation phenomenon considering a binary-sparse occurrence model on the error sequence. Simulation results show effective performance of the proposed scheme in comparison with the literature. © 1994-2012 IEEE
  6. Keywords:
  7. 1-Bit compressive sensing (CS) ; binary-sparse error correction ; level comparison (LC) sign measurements ; sparse signal acquisition ; Bins ; Compressed sensing ; Error correction ; Errors ; Estimation ; Feedback ; Iterative methods ; Mergers and acquisitions ; Receivers (containers) ; Signal receivers ; Signal reconstruction ; Uncertainty analysis ; Measurement uncertainty ; Prediction algorithms ; Signal processing algorithms ; Sparse signals ; Signal processing
  8. Source: IEEE Signal Processing Letters ; Volume 25, Issue 4 , 2018 , Pages 496-500 ; 10709908 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8281111