Statistics Shared CAF Diversity Combining Based Sensing Using Weight Computation Technique

Abstract

This paper presents weight computation techniques for spectrum sensing based on a cyclic autocorrelation function (CAF) shared diversity combining. We had reported that the performance of signal detection can be improved by the weight factor obtained from time-averaged of the CAF values, and the technique is based on cyclostationary detection based spectrum sensing. In the technique, time-averaged CAFs are used to extract a channel state information and compute a weight factor for the spectrum sensing based on the CAFs. However, the weight factor also includes the CAFs computed by purely additive white Gaussian noise, and the performance of signal detection degrades. In this paper, only the CAFs when it is judged that a primary user is presence are employed to obtain the time-averaged CAF. The presented results show that the performance of signal detection can be improved as compared with the conventional weight computation technique.