Spectrum Sensing in Cognitive Radio With Subspace Matching
Spectrum sensing has been put forward to make more efficient use of scarce radio frequency spectrum. The leading eigenvector of the sample covariance matrix has been applied to spectrum sensing under the frameworks of PCA and kernel PCA. In this paper, spectrum sensing with subspace matching is proposed. The subspace is comprised of the eigenvectors corresponding to dominant non-zero eigenvalues of the sample covariance matrix. That is, several eigenvectors are applied to spectrum sensing other than the only use of leading one. The distance between the subspaces is measured by the projection Frobenius norm. The simulations are done based on the simulated and captured DTV signals.