Collecting fusion gains for detection of spread spectrum signals using compressive wideband radios

In this paper, we investigate the possibility of improving the blind detection performance of direct sequence spread spectrum (DSSS) signals for cognitive radios (CRs). We consider a scenario where a wide range of frequency spectrum needs to be monitored by a single CR, and the presence of spread spectrum signals need to be identified reliably in a cost effective manner. We employ compressive sensing to achieve realistic sensing time without imposing excessive sampling-rate requirements on the analog-to-digital converter of the CR. We assume that the number, the center frequencies, and the spreading codes of the DSSS signals are unknown, but only the spread signal´s bandwidth is known. We propose a three-step algorithm for the CR. First, the power spectral density (PSD) of the wideband spectrum is estimated using compressed samples, and then detection is performed by thresholding to detect spectrum occupancy based on the estimated PSD. In the third step, knowledge of the spread signal´s bandwidth, and the estimated PSD are used to perform fusion by looking at spectrum occupancy at adjacent frequency bins using a sliding window. We present some simulation results illustrating the performance gain in detection achieved by introducing the fusion step. This is a useful result since it allows us to detect with improved performance the presence of multiple DSSS signals distributed over a very wideband spectrum, without requiring the knowledge of each signal´s spreading code.