Name
Abstract | ||
|---|---|---|
In this paper, a new spectrum sensing algorithm is proposed based on the eigenvalue distribution of the covariance matrix of sensing nodes. The received signals of all the nodes can be denoted by a non-Hermitian random matrix. A recent research indicates that the eigenvalue distribution for the product of non-Hermitian random matrices follows Single Ring Theorem for the noise-only case. However, for the signal-present case, the inner radius of the eigenvalue distribution is smaller than that of the noise-only case. Then mean spectral radius (MSR) can be utilized to detect the signal. The proposed method overcomes the noise uncertainty and has higher detection performance than the maximum-minimum eigenvalue (MME) detection when the primary signals among sensing nodes are uncorrelated. Finally, Simulations are performed to verify the effectiveness of the proposed method. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE 84TH VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL) | spectrum sensing, non-Hermitian random matrix, Single Ring Theorem, mean spectral radius, the eigenvalue distribution |
Field | DocType | ISSN |
Topology,Combinatorics,Spectral radius,Signal-to-noise ratio,Electronic engineering,Covariance matrix,Divide-and-conquer eigenvalue algorithm,Probability density function,Hermitian matrix,Mathematics,Eigenvalues and eigenvectors,Random matrix | Conference | 2577-2465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulong Gao | 1 | 0 | 3.72 |
| Xinsheng Han | 2 | 0 | 0.34 |
| Yongkui Ma | 3 | 48 | 8.93 |