A Fuzzy Hypothesis Testing Based Sample Covariance Matrix for Spectrum Sensing in Cognitive Radio Systems

Spectrum Sensing is a key technology in cognitive radio systems. Since orthogonal frequency division multiplexing (OFDM) is one of the major wideband transmission techniques, spectrum sensing of OFDM based primary user signals is considered in this work. This paper proposes a two-stage scheme-Fuzzy Hypothesis Testing based Sample Covariance Matrix (FHTSCM) for primary user signal detection. Firstly, a hypothesis testing scheme with p-value determination from the observed samples is used along with a fuzzy inference scheme for detecting primary user signals with SNR as low as -2dB. We recognize the equivalence of the probability of Type-I error and the probability of false alarm (Pfa) and use an important property of the p-value to form the fuzzy rule base. In the low SNR regime (less than -2dB), the second stage computes the sample covariance matrix from a limited number of observed samples and an appropriate test statistic is extracted and tested using Roy´s detector. Since the statistical covariances of the received signal and noise are usually different, they can be used to differentiate the case where the primary user signals are present from the one where there is only noise. The simulations are performed for the 2K mode of the DVB-T transmission and the probability of detection for different SNR levels is presented to verify the methods.