Weight Updating Technique in Spectrum Sensing Based on CAF Shared Diversity Combining

Abstract

This paper proposes weight updating techniques for spectrum sensing based on a cyclic autocorrelation function (CAF) shared diversity combining. We had reported that CAF shared diversity combining can improve the performance by the weight calculated from the time-averaged CAF value. However, the performance is degraded when the weight includes CAFs calculated from purely additive white Gaussian noise. To avoid this, this paper proposes the weight updating technique in which only the CAFs are employed to obtain the time-averaged CAF when it is judged that a primary user is present. This paper provides theoretical analysis results of the proposed technique. The proposed results show that the performance of signal detection can be improved as compared to the conventional technique.