Clustering scheme for cooperative spectrum sensing in cognitive radio networks

Abstract

For cognitive radio (CR), cooperation in spectrum sensing (SS) using energy detection can increase the detection of primary user signal. However, it also brings extra cooperative sensing overhead due to mutual exchange of large information among CR users. To reduce cooperative sensing overhead and improve sensing performance, the authors propose a novel clustering scheme consists of three stages: pruning, selecting, and clustering. In pruning stage, the CR users without sensing results will not join the following two stages; in the subsequent selecting stage, the CR user who has the most reliable sensing data is selected as the cluster header. For CR network, channel availability is an essence cause of cluster change. Hence, the authors proposed cluster constructing based on SS results correlation in the clustering stage. The simulation results show that the proposed clustering scheme not only has low overhead but also suit to perform in the low signal noise ratio environment.