Computationally Efficient Spectrum Sensing With Nyström Feature Template Matching

Kernel feature template matching (KFTM) was developed in 2014 for spectrum sensing in cognitive radio. However, the high computational burden involved in the kernel matrices construction and the related eigenvalues decomposition (EVD) limits its practical applications. This letter addresses this issue and proposes a Nyström feature template matching (NFTM) solution. Based on the randomly chosen subset of the whole data, the Nyström approximation is carried out to obtain the principle eigenvector of a given kernel matrix. With the approximated feature templates, we then modify the similarity (inner product) as a novel detection rule. Simulations using the real-world measurements of digital television (DTV) signal confirm that the proposed NFTM algorithm with about one-third samples provides almost the same performance as the KFTM method.