Performance Analysis of FISSER Model in Rural-Urban Cognitive Radio Networks. Issue 1 (August 2021)

• Record Type: Journal Article
• Authors: Oki, Olukayode; Olwal, Thomas; Adigun, Matthew
• Is Part Of: Journal of physics. Volume 1995:Issue 1(2021)
• Publication Date: 2021-08
• Subjects: FISSER -- Cognitive radio -- Foraging -- Spectrum reconfiguration -- Decision making; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1995/1/012013
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In the past few decades, Cognitive Radio network (CRN) has been regarded in the literature as the most promising technology for performing dynamic spectrum management. One of the major aspects of spectrum management is referred to as the spectrum-reconfiguration decision-making ability of CR users. Dynamic spectrum reconfiguration has previously been reported to improve the spectrum utilisation in CRN. In exploiting spectrum reconfiguration to improve spectrum utilisation, various approaches have been adopted to develop models. However, none of these research works has been evaluated in the urban and rural settlement context. In a CRN environment, the frequency availability is not static like that of traditional networks. There are resource-rich regions such as in rural areas, where many frequencies are available for the Secondary Users (SUs) and resource-poor regions such as urban areas, where there are only a few frequencies available for SUs. Hence, this paper proposed a novel Foraging Inspired Spectrum Selection and Reconfiguration (FISSER) model. The performance of the FISSER model has been analysed through computer simulations both in the rural and urban CRN, using high and low perceptual radii. A high perceptual radius has been shown in the literature to improve energy efficiency and data communication performance in ad hoc networks. However, the simulation results reported in this paper show that even though the high perceptual radius improves the nodes' energy efficiency it does not achieve optimal data communication performance compared to when the nodes use a low perceptual radius. Also, the efficacy of FISSER model was tested by comparing it against the reinforcement learning model. Based on the obtained results, it was shown that the FISSER model achieved better results in both the network throughput and the channel switching time.