The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Penderiaan spektrum adalah keperluan asas untuk radio kognitif, dan ia merupakan masalah yang mencabar dalam hingar impulsif yang dimodelkan oleh simetri alfa-stabil (SαS) pengagihan. Pengesan tenaga inti Gaussian (GKED) berprestasi lebih baik daripada pengesan konvensional dalam SαS bunyi yang diedarkan. Walau bagaimanapun, ia gagal untuk mengesan isyarat DC dan mempunyai kerumitan pengiraan yang tinggi. Untuk menyelesaikan masalah ini, kertas kerja ini mencadangkan pengesan yang lebih cekap dan mantap berdasarkan fungsi Gaussian (GF). Ungkapan analitikal pengesanan dan kebarangkalian penggera palsu diperoleh dan parameter terbaik untuk statistik dikira. Analisis teori dan hasil simulasi menunjukkan bahawa pengesan GF yang dicadangkan mempunyai kerumitan pengiraan yang jauh lebih rendah daripada kaedah GKED, dan ia boleh mengesan isyarat DC dengan jayanya. Di samping itu, pengesan GF berprestasi lebih baik daripada rakan sejawatan konvensional termasuk pengesan GKED dalam SαS bunyi yang diedarkan dengan eksponen ciri yang berbeza. Akhir sekali, kita membincangkan sebab mengapa pengesan GF mengatasi rakan sejawatan konvensional.
Jinjun LUO
National University of Defense Technology
Shilian WANG
National University of Defense Technology
Eryang ZHANG
National University of Defense Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
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
Salinan
@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},}
Salinan
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 -