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Becerra, J.A., Herrera, D., Madero-Ayora, M.J., et al. (2018) Sparse Model Selection of Digital Predistorters Using Subspace Pursuit. 13th European Microwave Integrated Circuits Conference, Madrid, 24-26 September 2018, 190-193.
https://doi.org/10.23919/EuMIC.2018.8539910

has been cited by the following article:

  • TITLE: Improved CoSaMP Reconstruction Algorithm Based on Residual Update

    AUTHORS: Dongxue Lu, Guiling Sun, Zhouzhou Li, Shijie Wang

    KEYWORDS: Compressed Sensing, Residual Descent, Reconstruction Algorithm, Backtracking

    JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.6, June 28, 2019

    ABSTRACT: A large number of sparse signal reconstruction algorithms have been continuously proposed, but almost all greedy algorithms add a fixed number of indices to the support set in each iteration. Although the mechanism of selecting the fixed number of indexes improves the reconstruction efficiency, it also brings the problem of low index selection accuracy. Based on the full study of the theory of compressed sensing, we propose a dynamic indexes selection strategy based on residual update to improve the performance of the compressed sampling matching pursuit algorithm (CoSaMP). As an extension of CoSaMP algorithm, the proposed algorithm adopts a residual comparison strategy to improve the accuracy of backtracking selected indexes. This backtracking strategy can efficiently select backtracking indexes. And without increasing the computational complexity, the proposed improvement algorithm has a higher exact reconstruction rate and peak signal to noise ratio (PSNR). Simulation results demonstrate the proposed algorithm significantly outperforms the CoSaMP for image recovery and one-dimensional signal.