Abstract—An improved spectrum sensing algorithm combining energy and eigenvalues is proposed, which employs the energy, maximum eigenvalue and minimum eigenvalue of the sample covariance matrix to construct test statistic. The proposed algorithm includes the MET, MME and EME algorithms as special cases, and it can be seen as a fusion of the test statistics of the MET and EME algorithms. In addition, the false alarm probability and threshold of the proposed method are derived using random matrix theory. The proposed algorithm is a more general algorithm. Simulation results show the effectiveness of the new algorithm.
Index Terms—Cognitive radio, eigenvalue based detection, random matrix theory, spectrum sensing.
H. Li, W. Zhao, and M. Jin are with the School of Information and Communication Engineering, Dalian University of Technology, Dalian, China (e-mail: aboutlihe@gmail.com, wenjingzhao2015@mail.dlut.edu.cn, mljin@dlut.edu.cn).
Sang-Jo Yoo is with the Department of Information and Communication Engineering, INHA University, Incheon, South Korea (e-mail: sjyoo@inha.ac.kr).
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Cite: He Li, Wenjing Zhao, Minglu Jin, and Sang-Jo Yoo, "Improved Spectrum Sensing Algorithm Combining Energy and Eigenvalues," International Journal of Future Computer and Communication vol. 9, no. 2, pp. 27-32, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
