Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing

作     者：Anal Paul Santi P. Maity 

作者机构：Department of Information TechnologyIndian Institute of Engineering Science and Technology Shibpur Howrah700 103 India 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2016年第2卷第4期

页      面：196-205页

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

主　　题：Cooperative spectrum sensing Kernel fuzzy c-means Energy detection Multiple PU detection 

摘      要：Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） has to handle a large set of data, a duster based approach, specifically fuzzy c-means clustering （FCM）, has been extensively used in energy detection based cooperative spectrum sensing （CSS）. However, the performance of FCM degrades at low signal-to-noise ratios （SNR） and in the presence of multiple PUs as energy data patterns at the FC are often found to be non-spherical i.e. overlapping. To address the problem, this work explores the scope of kernel fuzzy c-means （KFCM） on energy detection based CSS through the projection of non-linear input data to a high dimensional feature space. Extensive simulation results are shown to highlight the improved detection of multiple PUs at low SNR with low energy consumption. An improvement in the detection probability by ～6.78% and ～6.96% at -15 dBW and -20 dBW, respectively, is achieved over the existing FCM method.