A Study on Quantitative Parameters of Spectrum Handoff in Cognitive Radio Networks

Abstract

The innovation of wireless technologies requires dynamic allocation of spectrum band in an efficient manner. This has been achieved by Cognitive Radio (CR) networks which allow unlicensed users to make use of free licensed spectrum, when the licensed users are kept away from that spectrum. The cognitive radio makes decision, switching from primary user to secondary user and vice-versa, based on its built-in interference engine. It allows secondary users to makes use of a channel based on its availability i.e. on the absence of the primary user and they should vacate the channel once the primary user re-enters and continue their communication on another available channel and this process in the cognitive radio isknown as spectrum mobility. The main objective of spectrum mobility is that, there is no interruption caused due to the channel occupied by secondary users and maintains a good quality of service. In order to achieve better spectrum mobility, it is mandatory to choose an effective spectrum handoff strategy with the capability of predicting spectrum mobility. The handoff strategy with its parameters and its impact is an important concept in spectrum mobility but fairly explored. In this paper an empirical study on quantitative parameters involved in spectrum mobility prediction are discussed in detail. These parameters are studied extensively because they play a vital role in the spectrum handoff process moreover the impact of these parameters in various handoff methods can be used to predict the effectiveness of the system.

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

“…This has resulted in reconsideration of spectrum usage regulation by the government including the technology of spectrum access itself. Many research have considered overcoming this problem (Asuquo et al, 2020;Dhivya, and Murugesh, 2017;Zheng and Hua, 2016;Christian et al 2012;Ghasemi and Sousa, 2008), using reactive measures whereas few works exist on the use of proactive strategies (Ozturk et al, 2019;Devanarayana and Alfa, 2015;Dhivya et al 2013). With the current massive connectivity of mobile devices for IoTbased applications (Devaraj et al 2022;Tarek et al 2020), it becomes necessary to deploy appropriate channel allocation schemes to ensure fast end-to-end transmission of data packets, especially with resource-constrained devices.…”