Alternative Detectors for Spectrum Sensing by Exploiting Excess Bandwidth

Abstract

The problems regarding spectrum sensing are studied by exploiting a priori and a posteriori in information of the received noise variance. First, the traditional Average Likelihood Ratio (ALR) and the General Likelihood Ratio Test (GLRT) detectors are investigated under a Gamma distributed function as a channel noise, for the first time, under the availability of a priori statistical distribution about the noise variance. Then, two robust detectors are proposed using the exiting excess bandwidth to deliver a posteriori probability on the received noise variance uncertainty. The first proposed detector that is based on traditional ALR employs marginal distribution of the observation under available a priori and a posteriori of the received signal, while the second proposed detector employs the Maximum a posteriori (MAP) estimation of the inverse of the noise power under the same hypothesizes as the first detector. In addition, analytical expressions for the performance of the proposed detectors are obtained in terms of the false-alarm and detection probabilities. The simulation results exhibit the superiority of the proposed detectors over the traditional counterparts.