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Analytical and learning-based spectrum sensing time optimisation in cognitive radio systems
Sharif University of Technology . (Wireless Research Lab)ORCID iD: 0000-0001-6737-0266
Sharif University of Technology . (Wireless Research Lab)
2013 (English)In: IET Communications, ISSN 1751-8628, E-ISSN 1751-8636, Vol. 7, no 5, 480-489 p.Article in journal (Refereed) Published
Abstract [en]

In this study, the average throughput maximisation of a secondary user (SU) by optimising its spectrum sensing time is formulated, assuming that a priori knowledge of the presence and absence probabilities of the primary users (PUs) is available. The energy consumed to find a transmission opportunity is evaluated, and a discussion on the impacts of the number of PUs on SU throughput and consumed energy are presented. To avoid the challenges associated with the analytical method, as a second solution, a systematic adaptive neural network-based sensing time optimisation approach is also proposed. The proposed scheme is able to find the optimum value of the channel sensing time without any prior knowledge or assumption about the wireless environment. The structure, performance and cooperation of the artificial neural networks used in the proposed method are explained in detail, and a set of illustrative simulation results is presented to validate the analytical results as well as the performance of the proposed learning-based optimisation scheme.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2013. Vol. 7, no 5, 480-489 p.
Keyword [en]
Cognitive radio networks, spectrum sensing, average throughput, neural networks, energy efficiency
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-136461DOI: 10.1049/iet-com.2012.0302ISI: 000321732900011Scopus ID: 2-s2.0-84880647146OAI: oai:DiVA.org:kth-136461DiVA: diva2:676233
Note

Qc 20140219

Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2017-12-06Bibliographically approved

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