Title: Improved grey wolf optimisation based energy efficient spectral sensing in cognitive radio network

Authors: Praveen Hipparge; Shivkumar S. Jawaligi

Addresses: Faculty of Engineering and Technology, Department of Electronics and Communication Engineering, Sharnbasva University, Kalaburagi, Karnataka, 585105, India ' Faculty of Engineering and Technology, Department of Electronics and Communication Engineering, Sharnbasva University, Kalaburagi, Karnataka, 585105, India

Abstract: In a 5G heterogeneous network, the cognitive radio network (CRN) must choose amongst energy efficiency and spectrum sensing efficiency. When constructing a battery-powered CRN, energy efficiency is crucial. The major goal of existing techniques is to apply convex optimisation to solve the energy efficiency optimisation problem in spectrum sensing. Real-time spectrum sensing, nevertheless, is a non-convex optimisation issue. In context with this, we propose a novel improved grey wolf optimisation (IGWO) based approach to detect the enhanced energy usage spectrum holes to overcome the nonconvex issues. The cuckoo search algorithm is used to balance the exploitation and exploration phases of grey wolf optimisation (GWO). The energy efficient spectrum can be detected with the factors such as power spectral density, transmission power, and sensing bandwidth. Experimental results are compared with the state-of-art works. Our approach surpasses all the other works while considering the selection of energy efficient spectrum holes for the communication.

Keywords: spectrum; 5G network; CRN; cognitive radio network; optimisation; energy efficient.

DOI: 10.1504/IJSSE.2026.152421

International Journal of System of Systems Engineering, 2026 Vol.16 No.1, pp.17 - 30

Received: 17 Jul 2023
Accepted: 02 Oct 2023
Published online: 19 Mar 2026 *

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