Low-Complexity Blind Spectrum Sensing in Alpha-Stable Distributed Noise Based on a Gaussian Function

Jinjun LUO; Shilian WANG; Eryang ZHANG

doi:10.1587/transcom.2018EBP3250

Low-Complexity Blind Spectrum Sensing in Alpha-Stable Distributed Noise Based on a Gaussian Function

Jinjun LUO, Shilian WANG, Eryang ZHANG

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Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

Publication: IEICE TRANSACTIONS on Communications Vol.E102-B No.7 pp.1334-1344

Publication Date: 2019/07/01

Publicized: 2019/01/09

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2018EBP3250

Type of Manuscript: PAPER

Category: Antennas and Propagation

Authors

Jinjun LUO
  National University of Defense Technology
Shilian WANG
  National University of Defense Technology
Eryang ZHANG
  National University of Defense Technology

Keyword

spectrum sensing, impulsive noise, alpha stable distribution, Gaussian function, computational complexity

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Jinjun LUO, Shilian WANG, Eryang ZHANG, "Low-Complexity Blind Spectrum Sensing in Alpha-Stable Distributed Noise Based on a Gaussian Function" in IEICE TRANSACTIONS on Communications, vol. E102-B, no. 7, pp. 1334-1344, July 2019, doi: 10.1587/transcom.2018EBP3250.
Abstract: Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3250/_p

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@ARTICLE{e102-b_7_1334,
author={Jinjun LUO, Shilian WANG, Eryang ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Low-Complexity Blind Spectrum Sensing in Alpha-Stable Distributed Noise Based on a Gaussian Function},
year={2019},
volume={E102-B},
number={7},
pages={1334-1344},
abstract={Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.},
keywords={},
doi={10.1587/transcom.2018EBP3250},
ISSN={1745-1345},
month={July},}

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TY - JOUR
TI - Low-Complexity Blind Spectrum Sensing in Alpha-Stable Distributed Noise Based on a Gaussian Function
T2 - IEICE TRANSACTIONS on Communications
SP - 1334
EP - 1344
AU - Jinjun LUO
AU - Shilian WANG
AU - Eryang ZHANG
PY - 2019
DO - 10.1587/transcom.2018EBP3250
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2019
AB - Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.
ER -