Spectrum Sensing Based on Weighted Diversity Combining Using Time-Averaged CAF

Abstract

This paper proposes a cyclic autocorrelation function (CAF) diversity combining technique for spectrum sensing using test statistics shared among multiple receive antennas with time-averaged weights. The proposed technique computes a weight factor by averaging CAF, and the weight factor is employed to improve the performance of signal detection. The proposed results are compared with some conventional techniques, and they show that the signal detection performance can be improved without the increasing computational complexities in comparison with the conventional techniques.