Combination weighted clustering algorithms in cognitive radio networks. (9th September 2019)

• Record Type: Journal Article
• Authors: Li, Xiaoyan; Lv, Zhigang; Wang, Peng; Sun, Mengyu; Qiao, Mengyu
• Other Names: Xu Zheng guestEditor.; Zhou Qingyuan guestEditor.; Dong Fang guestEditor.; Yong Jianming guestEditor.; Fei Xiang guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 23(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-09-09
• Subjects: clustering algorithm -- cognitive radio -- combination weighting; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5516
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The topology of the distributed cognitive radio network is volatile as influenced by the behavior of primary users, and this condition leads to the large communication overhead and low utilization of spectrum resources. A combination weighted clustering algorithm is proposed in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. First, a clustering algorithm considering the available channel, geographic location, and experienced data (used for collecting the behavior of secondary users (SUs) in the network and the evaluation on it) of SUs is put forward through analyzing the characteristics of the idle channels in cognitive network. Three factors, namely, average channel capacity, stability, and channel quality, are converted into quantifiable values. The cluster head is determined on the basis of the three factors. Then, the cluster members and gateway nodes are determined using the weighting formula and the location information of the cluster head. Results show that the proposed clustering algorithm can generate 15% more clusters than other algorithms and reduce 40% of network communication overhead when the transmission distance between cognitive users and the channel number change. Thus, the stability of the cluster structure is maintained and the communication overhead is decreased. This study provides references for the construction of the stable distributed cognitive network.