Sequential Detector Design for Spectrum Sensing Considering Instantaneously Nonidentically Distributed Samples

Abstract

This paper studies signal detection methods in cognitive radios based on both the well-known likelihood ratio test (LRT) and the sequential probability ratio test (SPRT) considering instantaneously nonidentically distributed samples. Since it is rather impractical to perform the ideal test that fully reflects such a change of a sample statistic, we used a practical detection-method that optimally approximates the ideal log-likelihood ratio with respect to its statistical mean. In the LRT scenario, we found that there exists a nonnegligible performance gap between the ideal and practical tests, and the related experiments and theoretical analyses are also explored. Furthermore, we propose how to design a sequential detector in a robust manner to guarantee predefined false-alarm and missed-detection constraints for both the ideal and practical SPRTs.