Channel Error Detection based Cluster Formation for Cooperative Spectrum Sensing

Abstract

To improve the energy efficiency and network performance of the cognitive radio network, this paper has proposed an algorithm, which divides the whole network into few clusters. The clusters are made using fuzzy c-means (FCM) technique, while cluster heads (CHs) are chosen based on channel error detection technique. Delimited number of secondary users (SUs) are chosen from every cluster based on their channel performance. The sensing information of delegated SUs is delivered to their CHs, which is eventually sent for the final decision to fusion center (FC). The principal goal of cognitive radio network is sensing the spectrum, which specifies about the free spectrum hole. Hence SUs can take advantage of that spectrum, if it is not active. There are many factors that degrade the detection performance of network, i.e., multipath fading and shadowing. To figure out these problems, cooperative spectrum sensing methods were determined, in which all SUs of the whole network make cooperation and their sensing measurement is forwarded to the FC for the final decision of detection of primary user (PU). The enormous number of SUs involved in collaboration can create overhead for the FC. The proposed algorithm attains the apical energy and performance efficiency in comparison with conventional clustering methods.