Selectable sensing period optimisation for energy–constrained cognitive radio networks. (24th March 2015)

• Record Type: Journal Article
• Authors: Zhang, Long; Huang, Liusheng; Xu, Hongli
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 18:Number 3(2015)
• Page Start: 181
• Page End: 189
• Publication Date: 2015-03-24
• Subjects: cognitive radio networks -- spectrum sensing -- energy constraints -- selectable sensing period -- optimisation -- secondary users -- channel detection -- spectrum utilisation -- energy consumption -- simulation; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

Recently, cognitive radio has been proposed to improve the spectrum resource utilisation. In cognitive radio networks, the secondary (or unauthorised) users (SUs) are allowed to sense, detect and utilise the frequency bands that are not currently being used. Once the primary user (PU) is active, the secondary users have to vacate the channel within certain amount of time. Thus, it is a challenging task to find the suitable sensing period for better spectrum utilisation. In this paper, we propose a selectable sensing period optimisation (SSPO) mechanism. In the algorithm, each secondary user use the different periods for channel detection according to the different channel states. so that we can deal with the tradeoff between spectrum utilisation and energy consumption more flexibly. We consider the constraint of interference to PU and the energy consumption to compute the optimal sensing periods. Our simulation results demonstrate that, with the constraint of energy consumption, the SSPO algorithm can gain the spectrum utilisation 22:8% more than the previous algorithm. Moreover, with the constraint of spectrum utilisation, the SSPO algorithm can save the energy consumption 20:6% more than the previous algorithm.