Change search
ReferencesLink to record
Permanent link

Direct link
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 1751-8636, 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
URN: urn:nbn:se:kth:diva-136461DOI: 10.1049/iet-com.2012.0302ISI: 000321732900011ScopusID: 2-s2.0-84880647146OAI: diva2:676233

Qc 20140219

Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2014-02-21Bibliographically approved

Open Access in DiVA

fulltext(396 kB)129 downloads
File information
File name FULLTEXT02.pdfFile size 396 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopusPublished articlePublished version in IEEEXplore

Search in DiVA

By author/editor
Shokri-Ghadikolaei, Hossein
In the same journal
IET Communications
Communication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 129 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 45 hits
ReferencesLink to record
Permanent link

Direct link