Log Likelihood Ratio Test for Spectrum Sensing With Truncated Covariance Matrix
Publication Name
IEEE Internet of Things Journal
Abstract
Conventional auto-correlation based detectors often require the knowledge of a covariance matrix, which is usually replaced with its corresponding sample covariance matrix by dividing the received signal vector into a number of sub-vectors. This can lead to performance loss due to the deviation of the sample covariance matrix from the population covariance matrix. In this work, the received signal is used as a single signal vector and the use of sample covariance matrices is avoided. Taking advantage of oversampling, we obtain a truncated approximate covariance matrix of primary signals, which leads to a new approximate log-likelihood-ratio-test (aLLRT) detector with low complexity. In addition, a noise power estimator is also proposed by exploiting oversampling, which is incorporated into the new detector for practical implementation. Theoretical analyses for the false-alarm and detection probabilities of the proposed detector are conducted, and their accurate expressions are obtained via performing a nonlinear transformation to the test-statistic of the proposed detector. Numerical results show that, compared to state-of-the-art detectors, the proposed detector improves the detection probability by at least 10%.
Open Access Status
This publication is not available as open access