Effective sensor positioning to localize target transmitters in a Cognitive Radio Network

A precise positioning of transmitting nodes enhances the performance of Cognitive Radio (CR), by enabling more efficient dynamic allocation of channels and transmit powers for unlicensed users. Most localization techniques rely on random positioning of sensor nodes where, few sensor nodes may have a small separation between adjacent nodes. Closely spaced nodes introduces correlated observations, effecting the performance of Compressive Sensing (CS) algorithm. This paper introduces a novel minimum distance separation aided compressive sensing algorithm (MDACS). The algorithm selectively eliminates Secondary User (SU) power observations from the set of SU receiving terminals such that pairs of the remaining SUs are separated by a minimum geographic distance. We have evaluated the detection of multiple sparse targets locations and error in l2-norm of the recovery vector. The proposed method offers an improvement in detection ratio by 20% while reducing the error in l2-norm by 57%. Received on 19 June, 2015; accepted on 26 November, 2015; published on 05 April, 2016