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    Optimized Compressed Sensing Matrix Design for Noisy Communication Channels


    Shirazinia, Amirpasha and Dey, Subhrakanti (2015) Optimized Compressed Sensing Matrix Design for Noisy Communication Channels. In: 2015 IEEE International Conference on Communications (ICC). IEEE, pp. 4547-4552. ISBN 978-1-4673-6432-4

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    Abstract

    We investigate a power-constrained sensing matrix design problem for a compressed sensing framework. We adopt a mean square error (MSE) performance criterion for sparse source reconstruction in a system where the source-to-sensor channel and the sensor-to-decoder communication channel are noisy. Our proposed sensing matrix design procedure relies upon minimizing a lower-bound on the MSE. Under certain conditions, we derive closed-form solutions to the optimization problem. Through numerical experiments, by applying practical sparse reconstruction algorithms, we show the strength of the proposed scheme by comparing it with other relevant methods. We discuss the computational complexity of our design method, and develop an equivalent stochastic optimization method to the problem of interest that can be solved approximately with a significantly less computational burden. We illustrate that the low-complexity method still outperforms the popular competing methods.

    Item Type: Book Section
    Additional Information: Cite as: A. Shirazinia and S. Dey, "Optimized compressed sensing matrix design for noisy communication channels," 2015 IEEE International Conference on Communications (ICC), 2015, pp. 4547-4552, doi: 10.1109/ICC.2015.7249039.
    Keywords: Optimized; Compressed; Sensing Matrix Design; Noisy Communication Channels;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14533
    Identification Number: https://doi.org/10.1109/ICC.2015.7249039
    Depositing User: Subhrakanti Dey
    Date Deposited: 15 Jun 2021 14:09
    Publisher: IEEE
    Refereed: Yes
    URI:

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