Spatio-Temporal Fusion for Small-Scale Primary Detection in Cognitive Radio Networks

In Cognitive Radio Networks (CRNs), detecting small-scale primary devices - such as Wireless Microphones (WMs) - is a challenging, but very important, problem that has not yet been addressed well. The authors identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary devices. In particular, they derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. The authors also observe that the sensing performance is sensitive to the accuracy in estimating the primary's location and transmit-power. Based on these observations, they propose an efficient sensing framework, called DeLOC, which iteratively performs location/transmit-power estimation and dynamic sensor selection for cooperative sensing.