Spectrum Selection Technique to Reduce the Number of Channel Switching for Dynamic Spectrum Access in Cognitive Radio Networks

Adebayo I. Aderonmu, Linoh A. Magagula, and Djouani Karim


Cognitive Radio, Radio Frequency, Channel Switching, Smart Learning Mechanism


The awareness of Cognitive Radio (CR) was introduced to increase the effectiveness and efficiency of spectrum consumption. In a Cognitive Radio Network (CRN), each secondary user (SU) is expected to select the best radio frequency (RF) spectrum band for opportunistic use when the primary users (PUs) have temporarily vacated the spectrum allocated to them. Many spectrum selection techniques have been proposed in the literature to select vacant spectra in CRNs. However, most of these methods do not adequately consider the effect that frequent channelswitching might have on the quality of service (QoS) requirements of the SUs. In additional, the channel usage arrangement over time by PUs is not considered. In this paper, we propose a heuristic-based spectrum selection technique (HBSST) with the aim of selecting the best available channel for use by SU without causing harmful interference to the PU in a cognitive radio network (CRN) with minimum number of channel switchings. We used a smart learning mechanism to obtain the spectral opportunities in the primary network since the channel usage by PUs follow a deterministic time arrangement. Results shows that HBSST out performs the Random spectrum selection scheme in terms of channel switching.

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