A Comparability Based Adaptive Clustering Algorithm in Cognitive Radio Network
Guo Caili · Beijing Youdian Xueyuan xuebao · 2008
According to the characteristic of real-time changes of available channels in cognitive radio network,a new comparability based adaptive clustering algorithm(CBAC) in application of graph theory is proposed.Based on the comparability of users' available channels and the consideration of mobility of cognitive radio users,the algorithm optimizes the clustering result in cognitive radio network via computing the node's weight.Experiments show that the CBAC algorithm increases the number of the link's average available channels and has higher spectrum utilization rate and lower communication overhead than that of traditional clustering algorithms.