Spatial Spectrum Based Spectrum Sensing Algorithm and Performance Analysis

DANG Xiaoyu; LI Aming; YU Xiangbin

doi:10.11999/JEIT150823

Volume 38 Issue 5

May 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(5): 1179-1185

DANG Xiaoyu, LI Aming, YU Xiangbin. Spatial Spectrum Based Spectrum Sensing Algorithm and Performance Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1179-1185. doi: 10.11999/JEIT150823

Citation:

DANG Xiaoyu, LI Aming, YU Xiangbin. Spatial Spectrum Based Spectrum Sensing Algorithm and Performance Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1179-1185. doi: 10.11999/JEIT150823

Citation:

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Spatial Spectrum Based Spectrum Sensing Algorithm and Performance Analysis

doi: 10.11999/JEIT150823

Funds:

The National Natural Science Foundation of China (61172078, 61201208), The State Education Ministry Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars and the Fundamental Research Funds for the Central Universities (NS2014038), The Foundation of Graduate Innovation Center in NUAA (kfjj20150404)

Received Date: 2015-07-09
Rev Recd Date: 2015-12-02
Publish Date: 2016-05-19

Abstract

Abstract

Spectrum sensing algorithms based on eigenvalue or spectral density usually use the Gaussian approximated distribution and Tracy-Widom distribution to analyze the test statistic with the presence of the primary user or not respectively, but it is hard to find the analysis expression with unified form. In this paper, a spectrum sensing algorithm is proposed based on spatial spectrum density ratio using a Uniform Linear Array (ULA), and a unified expression for the distribution of test statistic is proposed using the latest research results of order statistics. In this algorithm, the test statistic is established using the maximum and minimum values of the discrete spatial spectrum density. Simulation results show that the performance of the proposed algorithm is about 1.7 dB better than the Maximum-Minimum Eigenvalue (MME) ratio algorithm with the detection probability equal to 0.9. At the same time, the results also verify the accuracy of the theoretical distribution of the test statistic.
- Cognitive Radio (CR),
- Spectrum sensing,
- Uniform Linear Array (ULA),
- Order statistics

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References(20)

References

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