Study on Cooperative Spectrum Sensing Algorithm Based on Random Matrix Non-asymptotic Spectral Theory

XU Weiyang; LI Youjun; XU Hongqian; XIE Huiqiang

doi:10.11999/JEIT170309

Volume 40 Issue 1

Jan. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 123-129

XU Weiyang, LI Youjun, XU Hongqian, XIE Huiqiang. Study on Cooperative Spectrum Sensing Algorithm Based on Random Matrix Non-asymptotic Spectral Theory[J]. Journal of Electronics & Information Technology, 2018, 40(1): 123-129. doi: 10.11999/JEIT170309

Citation:

XU Weiyang, LI Youjun, XU Hongqian, XIE Huiqiang. Study on Cooperative Spectrum Sensing Algorithm Based on Random Matrix Non-asymptotic Spectral Theory[J]. Journal of Electronics & Information Technology, 2018, 40(1): 123-129. doi: 10.11999/JEIT170309

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Study on Cooperative Spectrum Sensing Algorithm Based on Random Matrix Non-asymptotic Spectral Theory

doi: 10.11999/JEIT170309

Funds:

The National Natural Science Foundation of China (61201177)

Received Date: 2017-04-07
Rev Recd Date: 2017-09-01
Publish Date: 2018-01-19

Abstract

Abstract

The non-asymptotic spectral theory of random matrix is applied to cooperative spectrum sensing, the maximum eigenvalue and the minimum eigenvalue of the sampled signal covariance matrix are analyzed and an Exact Maximum Minimum Eigenvalues Difference (EMMED) algorithm is proposed. For any given numbers of cooperative users K and sampling points N, the exact Probability Density Function (PDF) and Cumulative Distribution Function (CDF) of the difference between the maximum and minimum eigenvalues are derived. Then, an accurate decision threshold is designed by using the distribution function. Theoretical analysis shows, the EMMED algorithm has the characteristics that the decision threshold is more accurate than the existing Asymptotic Maximum Minimum Eigenvalue Difference (AMMED) algorithm, without the characteristics of the main user signal and not affected by noise uncertainty. In addition, the simulation results show that the EMMED algorithm has better detection performance than the existing Exact Maximum Eigenvalue (EME) and EMMER algorithms in the real sensing environment with noisy uncertainty.
- Spectrum sensing,
- Random matrix,
- Non-asymptotic spectral theory,
- Maximum minimum eigenvalue

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References

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