Efficient Algorithm for 1-Bit Compressed Sensing with Multiple Measurement 


Vol. 47,  No. 9, pp. 1253-1259, Sep.  2022
10.7840/kics.2022.47.9.1253


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  Abstract

In this paper, we consider a compressed sensing (CS) problem with multiple measurement vector (MMV), in which a set of sparse signals with the same support are recovered simultaneously from the corresponding measurements. Many practical problems (e.g., active user detection for massive connectivity, communication-efficient federated learning, and so on) have been formulated in this problem. Also, it is necessary to investigate one-bit CS under the MMV framework, in order to boost communication efficiency. Unfortunately, the best-known one-bit CS algorithms such as Bayesian matching pursuit (BMP) is not applicable to the emerging one-bit MMV problems. In these problems, we propose novel algorithms, named Turbo-BMP as nontrivial extensions of BMP, respectively. Simulation results demonstrate the superiority of the proposed algorithm.

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[IEEE Style]

Y. Noh and S. Hong, "Efficient Algorithm for 1-Bit Compressed Sensing with Multiple Measurement," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 9, pp. 1253-1259, 2022. DOI: 10.7840/kics.2022.47.9.1253.

[ACM Style]

Yerim Noh and Songnam Hong. 2022. Efficient Algorithm for 1-Bit Compressed Sensing with Multiple Measurement. The Journal of Korean Institute of Communications and Information Sciences, 47, 9, (2022), 1253-1259. DOI: 10.7840/kics.2022.47.9.1253.

[KICS Style]

Yerim Noh and Songnam Hong, "Efficient Algorithm for 1-Bit Compressed Sensing with Multiple Measurement," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 9, pp. 1253-1259, 9. 2022. (https://doi.org/10.7840/kics.2022.47.9.1253)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880