Spectrum Sensing Based on Learned Iterative Shrinkage Thresholding Algorithm for Sparse Wideband Signals 


Vol. 44,  No. 11, pp. 2041-2046, Nov.  2019
10.7840/kics.2019.44.11.2041


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  Abstract

In this paper, we propose a spectrum sensing scheme based on the learned iterative shrinkage thresholding algorithm for wideband signals with sparse characteristics in the frequency domain. There is a limitation in implementing an analog-to-digital converter that samples a wideband signal at rates above the Nyquist rate. Compressed sensing is a signal processing technique that samples a sparse signal at rates below the Nyquist rate and recovers the original signal from the sampled signal. We transform the compressed signal into a frame for recovery using LISTA and detect the occupied frequency bands through the recovered signal. The proposed scheme has lower complexity than the conventional scheme and has lower miss-detection probability in the low sparse case.

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  Cite this article

[IEEE Style]

D. Kim, J. Ahn, D. Park, "Spectrum Sensing Based on Learned Iterative Shrinkage Thresholding Algorithm for Sparse Wideband Signals," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 11, pp. 2041-2046, 2019. DOI: 10.7840/kics.2019.44.11.2041.

[ACM Style]

Dohyun Kim, Junil Ahn, and Daeyoung Park. 2019. Spectrum Sensing Based on Learned Iterative Shrinkage Thresholding Algorithm for Sparse Wideband Signals. The Journal of Korean Institute of Communications and Information Sciences, 44, 11, (2019), 2041-2046. DOI: 10.7840/kics.2019.44.11.2041.

[KICS Style]

Dohyun Kim, Junil Ahn, Daeyoung Park, "Spectrum Sensing Based on Learned Iterative Shrinkage Thresholding Algorithm for Sparse Wideband Signals," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 11, pp. 2041-2046, 11. 2019. (https://doi.org/10.7840/kics.2019.44.11.2041)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880