Research Of Primary Users Localization Algorithm In Cognitive Radio Networks

With dramatic development of wireless communication technologies, radio spectrum recourses are becoming more and more scarce. Cognitive Radio is deemed to be a promising technology to improve the spectrum utilization. The localization technique for primary users plays a key role in the Cognitive Radio. In Cognitive Radio networks, the Secondary Users (SU) may observe different profiles of spectrum holes due to different distances from the Primary Users (PU). When the location information of PUs is given, the SUs can evaluate and most effectively use their spectrum-access opportunities without harmful interference to the primary system. On the other hand, with the knowledge of the PUs’position, the cognitive radio can also provide some location-based value added services, execute location-aided Network Optimization.By analyzing several WSN (wireless sensor network) localization algorithms and combining with the technical advantages of cognitive radio, a new primary users localization algorithm based on received signal strength is proposed, through which we can calculate the location of primary user. Furthermore, we proposed RSSI based K-Means clustering localization algorithm, which utilize all the intersection points of circles to find location of primary user. The algorithm is simple and does not need to increase the communication overhead and hardware expansion.Secondly. We have presented an EM-based technique for estimating the locations of multiple transmitters based on measurements of signal strength. The algorithm is applicable to cognitive radio environments in which no cooperation can be assumed between PUs and SUs. It should be noted that, while we measure performance in terms of the accuracy of transmitter location estimates, the effect of transmitter localization accuracy on the overall performance of the cognitive radio system is of greater interest.We have proposed a blind multiple primary users localization technology which using SVD method to estimate the number of PUs firstly and applying k-mean clustering and iterative processing to determine the positions of PUs. The technique is applicable to estimate the location of number-unkown multiple un-cooperative PUs only from observed data at several secondary sensors.