Partial Covariance-Based Detectors for Cooperative Spectrum Sensing in Cognitive Communications.

This article proposes modified test statistics for six blind covariance-based detectors used in data fusion cooperative spectrum sensing, where the full Hermitian sample covariance matrix (SCM) of the received signal is replaced by a symmetric real-valued partial sample covariance matrix (PSCM). This substitution results in a substantial reduction in overall computational complexity compared to the original SCM-based formulations, while preserving or improving detection accuracy under realistic conditions that include non-uniform noise powers, time-varying distance-dependent path loss, spatially correlated shadowing, and multipath fading with a random Rice factor. The computation of the PSCM requires 50% fewer floating-point operations than the full SCM and offers a hardware-friendly structure due to its reliance on real-valued arithmetic. On the test statistic side, the adoption of the PSCM leads to computational costs ranging from 3.37% to 61.9% of those incurred by the corresponding SCM-based test statistics.