Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing
Anal Paul, Santi P. Maity · Digital Communications and Networks · 2016
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 cluster 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.