Spectrum Sensing by Exploiting the Similarity of PDFs of Two Time-Adjacent Detected Data Sets with Cross Entropy

Junrong GU; Wenlong LIU; Sung Jeen JANG; Jae Moung KIM

doi:10.1587/transcom.E94.B.3623

Spectrum Sensing by Exploiting the Similarity of PDFs of Two Time-Adjacent Detected Data Sets with Cross Entropy

Junrong GU, Wenlong LIU, Sung Jeen JANG, Jae Moung KIM

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In spectrum sensing, if the primary user (PU) signal and the channel noise both follow Gaussian distribution and neither of their probability distribution functions (PDFs) are known, the traditional approaches based on entropy or Likelihood Ratio Test (LRT) etc., become infeasible. To address this problem, we propose a spectrum sensing method that exploits the similarity of PDFs of two time-adjacent detected data sets with cross entropy, while accounting for achieving the detection performance of LRT which is Neyman-Pearson optimal in detecting the primary user. We show that the detection performance of the proposed method asymptotically approximates that of LRT in detecting the PU. The simulation results confirm our analysis.

Publication: IEICE TRANSACTIONS on Communications Vol.E94-B No.12 pp.3623-3626

Publication Date: 2011/12/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E94.B.3623

Type of Manuscript: LETTER

Category: Terrestrial Wireless Communication/Broadcasting Technologies

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Junrong GU, Wenlong LIU, Sung Jeen JANG, Jae Moung KIM, "Spectrum Sensing by Exploiting the Similarity of PDFs of Two Time-Adjacent Detected Data Sets with Cross Entropy" in IEICE TRANSACTIONS on Communications, vol. E94-B, no. 12, pp. 3623-3626, December 2011, doi: 10.1587/transcom.E94.B.3623.
Abstract: In spectrum sensing, if the primary user (PU) signal and the channel noise both follow Gaussian distribution and neither of their probability distribution functions (PDFs) are known, the traditional approaches based on entropy or Likelihood Ratio Test (LRT) etc., become infeasible. To address this problem, we propose a spectrum sensing method that exploits the similarity of PDFs of two time-adjacent detected data sets with cross entropy, while accounting for achieving the detection performance of LRT which is Neyman-Pearson optimal in detecting the primary user. We show that the detection performance of the proposed method asymptotically approximates that of LRT in detecting the PU. The simulation results confirm our analysis.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.3623/_p

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@ARTICLE{e94-b_12_3623,
author={Junrong GU, Wenlong LIU, Sung Jeen JANG, Jae Moung KIM, },
journal={IEICE TRANSACTIONS on Communications},
title={Spectrum Sensing by Exploiting the Similarity of PDFs of Two Time-Adjacent Detected Data Sets with Cross Entropy},
year={2011},
volume={E94-B},
number={12},
pages={3623-3626},
abstract={In spectrum sensing, if the primary user (PU) signal and the channel noise both follow Gaussian distribution and neither of their probability distribution functions (PDFs) are known, the traditional approaches based on entropy or Likelihood Ratio Test (LRT) etc., become infeasible. To address this problem, we propose a spectrum sensing method that exploits the similarity of PDFs of two time-adjacent detected data sets with cross entropy, while accounting for achieving the detection performance of LRT which is Neyman-Pearson optimal in detecting the primary user. We show that the detection performance of the proposed method asymptotically approximates that of LRT in detecting the PU. The simulation results confirm our analysis.},
keywords={},
doi={10.1587/transcom.E94.B.3623},
ISSN={1745-1345},
month={December},}

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TY - JOUR
TI - Spectrum Sensing by Exploiting the Similarity of PDFs of Two Time-Adjacent Detected Data Sets with Cross Entropy
T2 - IEICE TRANSACTIONS on Communications
SP - 3623
EP - 3626
AU - Junrong GU
AU - Wenlong LIU
AU - Sung Jeen JANG
AU - Jae Moung KIM
PY - 2011
DO - 10.1587/transcom.E94.B.3623
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2011
AB - In spectrum sensing, if the primary user (PU) signal and the channel noise both follow Gaussian distribution and neither of their probability distribution functions (PDFs) are known, the traditional approaches based on entropy or Likelihood Ratio Test (LRT) etc., become infeasible. To address this problem, we propose a spectrum sensing method that exploits the similarity of PDFs of two time-adjacent detected data sets with cross entropy, while accounting for achieving the detection performance of LRT which is Neyman-Pearson optimal in detecting the primary user. We show that the detection performance of the proposed method asymptotically approximates that of LRT in detecting the PU. The simulation results confirm our analysis.
ER -