Differential Evolution in PFCM Clustering for Energy Efficient Cooperative Spectrum Sensing

  • Anal Paul
  • Santi P. Maity
Part of the Studies in Computational Intelligence book series (SCI, volume 687)


Cooperative spectrum sensing (CSS) in cognitive radio network (CRN) is highly recommended to avoid the interference from secondary users (SUs) to primary user (PU). Several studies report that clustering-based CSS technique improves the system performance, among them fuzzy c-means (FCM) clustering algorithm is widely explored. However, it is observed that FCM generates an improper clustering of sensing information at low signal-to-noise ratio (SNR) due to inseparable nature of energy data set. To address this problem, the present chapter describes a work that investigates the scope of possibilistic fuzzy c-means (PFCM) algorithm on energy detection-based CSS. PFCM integrates the possibilistic information and fuzzy membership values of input data in the clustering process to segregate the indistinguishable energy data into the respective clusters. Differential evolution (DE) algorithm is applied with PFCM to maximize the probability of PU detection (\(P_D\)) under the constraint of a target false alarm probability (\(P_{fa}\)). The present work also evaluates the required power consumption during CSS by SUs. The proposed technique improves \(P_D\) by \(\sim \!12.53\%\) and decreases average energy consumption by \(\sim \!5.34\%\) over the existing work.


Spectrum sensing Fuzzy c-means clustering Possibilistic fuzzy c-means clustering Differential evolution algorithm 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyIndian Institute of Engineering Science and Technology, ShibpurHowrahIndia

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