Adaptive Particle Swarm Optimisation based Energy Efficient Dynamic Correlation Behavior of Secondary Nodes in Cognitive Radio Sensor Networks. Issue 10 (1st June 2020)

• Record Type: Journal Article
• Authors: Mukherjee, Amrit; Goswami, Pratik; Yan, Ziwei; Yang, Lixia
• Is Part Of: IET communications. Volume 14:Issue 10(2020)
• Page Start: 1658
• Page End: 1665
• Publication Date: 2020-06-01
• Subjects: wireless sensor networks -- cooperative communication -- signal detection -- particle swarm optimisation -- radio spectrum management -- cognitive radio -- Gaussian processes -- telecommunication power management -- energy conservation; energy efficient dynamic correlation behaviour -- secondary nodes cognitive radio sensor networks -- wireless sensor network -- wireless communication -- cognitive capabilities -- efficient spectrum usage -- secondary users -- statistical behaviour -- uneven communication -- repetitive communication -- cooperative communication scenario -- advanced particle swarm optimisation algorithm -- time‐varying spectrum sensing behaviour -- multiple SUs -- time delay -- spectrum sensing results -- Gaussian copula theory; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.0750
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Wireless sensor network enhances the classic features of wireless communication with cognitive capabilities for efficient spectrum usage. This work focuses on the dynamic correlation between the secondary users (SUs) based on their statistical behaviour while performing the cooperative communication in cognitive radio sensor network. The proposed approach addresses the problem of uneven and repetitive communication between the SUs in a cooperative communication scenario. The authors' objective is to use a novel approach based on the Gaussian copula theory and advanced particle swarm optimisation algorithm to analyse the dependencies of time‐varying spectrum sensing behaviour of multiple SUs. Here, time delay in prediction reduces due to the analysis of the dynamic correlation between the time delay in spectrum sensing results for the same set of channels. The simulation results show the performance of the proposed approach outperforming the other well‐known techniques.