IEICE TRANSACTIONS on Communications
Weighted Hard Combination for Cooperative Spectrum Sensing under Noise Uncertainty
Summary :
Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E97-B No.2 pp.275-282
- Publication Date
- 2014/02/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E97.B.275
- Type of Manuscript
- Special Section PAPER (Special Section on Technologies for Effective Utilization of Spectrum White Space)
- Category
Authors
Ruyuan ZHANG
Tsinghua University
Yafeng ZHAN
Tsinghua University
Yukui PEI
Tsinghua University
Jianhua LU
Tsinghua University
Keyword
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Ruyuan ZHANG, Yafeng ZHAN, Yukui PEI, Jianhua LU, "Weighted Hard Combination for Cooperative Spectrum Sensing under Noise Uncertainty" in IEICE TRANSACTIONS on Communications,
vol. E97-B, no. 2, pp. 275-282, February 2014, doi: 10.1587/transcom.E97.B.275.
Abstract: Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E97.B.275/_p
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@ARTICLE{e97-b_2_275,
author={Ruyuan ZHANG, Yafeng ZHAN, Yukui PEI, Jianhua LU, },
journal={IEICE TRANSACTIONS on Communications},
title={Weighted Hard Combination for Cooperative Spectrum Sensing under Noise Uncertainty},
year={2014},
volume={E97-B},
number={2},
pages={275-282},
abstract={Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.},
keywords={},
doi={10.1587/transcom.E97.B.275},
ISSN={1745-1345},
month={February},}
Copy
TY - JOUR
TI - Weighted Hard Combination for Cooperative Spectrum Sensing under Noise Uncertainty
T2 - IEICE TRANSACTIONS on Communications
SP - 275
EP - 282
AU - Ruyuan ZHANG
AU - Yafeng ZHAN
AU - Yukui PEI
AU - Jianhua LU
PY - 2014
DO - 10.1587/transcom.E97.B.275
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E97-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2014
AB - Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.
ER -
English
