Simple Diversity Combining Techniques for Cyclostationarity Detection Based Spectrum Sensing in Cognitive Radio Networks

This paper presents simple diversity combining techniques for cyclostationarity detection based spectrum sensing in cognitive radio networks. The presented techniques are based on maximum cyclic autocorrelation function (MCAS) techniques. The MCAS judges whether received signals include an orthogonal frequency division multiplexing (OFDM) signals or not, by comparing the peak and non-peak values of a cyclic autocorrelation function (CAF). The presented diversity techniques attempt to increase signal-to-noise ratio (SNR) of CAF which is composed of the peak and non-peak values of CAF. In the presented techniques, the CAF SNRs which obtained at some received antennas are combined whereas general diversity combining techniques combines some received signals. The presented results are compared with some conventional results, and computational and theoretical analysis results show that the presented techniques can improve the spectrum sensing performance.