Sliding Window-Based Spectrum Sensing with Deep Learning for Pulse Radar Signals 


Vol. 49,  No. 9, pp. 1236-1239, Sep.  2024
10.7840/kics.2024.49.9.1236


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  Abstract

A sliding window-based spectrum sensing method determines the presence or absence of a primary user by comparing the maximum of the received signal energies from multiple sliding windows with a threshold. In this letter, aiming to enhance this scheme, we present a deep learning-based approach for exploiting the pattern of the received signal energies from sliding windows and investigate its sensing performance.

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[IEEE Style]

C. H. Lim and J. Kim, "Sliding Window-Based Spectrum Sensing with Deep Learning for Pulse Radar Signals," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 9, pp. 1236-1239, 2024. DOI: 10.7840/kics.2024.49.9.1236.

[ACM Style]

Chang Heon Lim and Jin-Yul Kim. 2024. Sliding Window-Based Spectrum Sensing with Deep Learning for Pulse Radar Signals. The Journal of Korean Institute of Communications and Information Sciences, 49, 9, (2024), 1236-1239. DOI: 10.7840/kics.2024.49.9.1236.

[KICS Style]

Chang Heon Lim and Jin-Yul Kim, "Sliding Window-Based Spectrum Sensing with Deep Learning for Pulse Radar Signals," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 9, pp. 1236-1239, 9. 2024. (https://doi.org/10.7840/kics.2024.49.9.1236)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880