Cooperative Spectral Covariance Sensing Under Correlated Shadowing
This paper investigates the theoretical limits of white space sensing in a Cognitive Radio (CR) network limited by channel correlation. In a log-normal shadowing channel, the received signal power is correlated based on the distance between the sensors and this makes sensing the presence of a signal difficult, even with several cooperative sensors. In the proposed system, each sensor uses the Spectral Covariance Sensing (SCS) algorithm to detect the primary signal and then sends its decision statistic to the Base Station (BS). The BS, using the Neyman-Pearson log-likelihood ratio test, makes the final decision. The authors analyze the Probability of a False Alarm (PFA) and compare it with that of the cooperative energy detector.