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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2021

08.02.2021

An Efficient Opposition Based Grey Wolf Optimizer for Weight Adaptation in Cooperative Spectrum Sensing

verfasst von: Avneet Kaur, Surbhi Sharma, Amit Mishra

Erschienen in: Wireless Personal Communications | Ausgabe 4/2021

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Abstract

This paper presents an integrated meta-heuristic technique, namely opposition based grey wolf optimizer (OBGWO) and demonstrates its application for optimizing the sensing performance of cooperative spectrum sensing (CSS) scheme in cognitive radio (CR) system. The proposed technique improves the search ability of grey wolf optimizer (GWO) by integrating it with the concept of opposition based learning. Further, the competence of OBGWO is tested on seven benchmark functions and its performance is compared with other existing meta-heuristic techniques. Simulation results demonstrate that OBGWO provides better solutions and improved convergence characteristics when compared with GWO, sine–cosine algorithm and moth flame optimization algorithm. Subsequently, the proposed scheme when applied to weight vector optimization for CSS; results in higher probability of detection for a given probability of false alarm.
Vorheriger Artikel Energy-Efficient Load Balancing Strategy for Wireless Sensor Networks using Quasi-oppositional based Jaya Optimization
Nächster Artikel A New UTD Based Time-Domain Solution for UWB Diffraction in 3-D Environments

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Metadaten
Titel
An Efficient Opposition Based Grey Wolf Optimizer for Weight Adaptation in Cooperative Spectrum Sensing
verfasst von
Avneet Kaur
Surbhi Sharma
Amit Mishra
Publikationsdatum
08.02.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-08129-4

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