Application‐specific spectrum sensing method for cognitive sensor networks. Issue 3 (1st September 2013)

• Record Type: Journal Article
• Authors: Zahmati, Amir Sepasi; Fernando, Xavier; Grami, Ali
• Is Part Of: IET wireless sensor systems. Volume 3:Issue 3(2013)
• Page Start: 193
• Page End: 204
• Publication Date: 2013-09-01
• Subjects: cognitive radio -- continuous time systems -- Markov processes -- minimisation -- radio spectrum management -- radiofrequency interference -- signal detection -- wireless sensor networks -- telecommunication power management; primary and secondary users -- throughput maximisation -- power consumption minimisation -- network power consumption reduction -- optimal sensing period -- undetected opportunity ratio -- interference ratio -- hybrid scheme -- application specific spectrum sensing method -- energy detection method -- continuous‐time Markov chain model -- sensing period optimisation -- CR -- cognitive radio -- cognitive sensor networks; Wireless sensor networks -- Periodicals; 681.2
• DOI: 10.1049/iet-wss.2013.0006
• Languages: English
• ISSNs: 2043-6386

Abstract

Abstract : The authors address an important aspect of spectrum sensing that has been often overlooked in the cognitive radio (CR) research. Although CR is supposed to be aware of its surrounding, most existing articles do not consider the characteristics of secondary users in the optimisation of sensing period. In this study, based on a continuous‐time Markov chain model for cognitive sensor networks and energy detection method, the authors propose an application‐specific spectrum sensing method that obtains the optimal sensing period according to the characteristics of both 'primary and secondary' users (hybrid scheme). The authors define and analytically derive two parameters, the interference ratio and the undetected opportunity ratio, and analytically find the optimum sensing period. Numerical and simulation results indicate that our proposed method is able to provide an optimal sensing period, that is customised for different cognitive networks. The proposed method significantly increases the system throughput by up to 11% and reduces the network's power consumption by as low as 33%. Finally, the trade‐off between the throughput maximisation and power consumption minimisation is discussed.