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Analysis and optimization of random sensing order in cognitive radio networks
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6737-0266
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9810-3478
2014 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 33, no 5, 803-819 p.Article in journal (Refereed) Published
Abstract [en]

Developing an efficient spectrum access policy enables cognitive radios to dramatically increase spectrum utilization while ensuring predetermined quality of service levels for primary users. In this paper, modeling, performance analysis, and optimization of a distributed secondary network with random sensing order policy are studied. Specifically, the secondary users create a random order of available channels upon primary users return, and then find optimal transmission and handoff opportunities in a distributed manner. By a Markov chain analysis, the average throughputs of the secondary users and average interference level among the secondary and primary users are investigated. A maximization of the secondary network performance in terms of the throughput while keeping under control the average interference is proposed. It is shown that despite of traditional view, non-zero false alarm in the channel sensing can increase channel utilization, especially in a dense secondary network where the contention is too high. Then, two simple and practical adaptive algorithms are established to optimize the network. The second algorithm follows the variations of the wireless channels in non-stationary conditions and outperforms even static brute force optimization, while demanding few computations. The convergence of the distributed algorithms are theoretically investigated based on the analytical performance indicators established by the Markov chain analysis. Finally, numerical results validate the analytical derivations and demonstrate the efficiency of the proposed schemes. It is concluded that fully distributed sensing order algorithms can lead to substantial performance improvements in cognitive radio networks without the need of centralized management or message passing among the users.

Place, publisher, year, edition, pages
IEEE Press, 2014. Vol. 33, no 5, 803-819 p.
Keyword [en]
Cognitive radio networks, sequential channel sensing, Markov chain analysis, dense and ultra dense networks, distributed optimization
National Category
Telecommunications Communication Systems
Research subject
Electrical Engineering; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157752DOI: 10.1109/JSAC.2014.2361077ISI: 000353565800005Scopus ID: 2-s2.0-84928737890OAI: oai:DiVA.org:kth-157752DiVA: diva2:771567
Note

QC 20141219

Available from: 2014-12-14 Created: 2014-12-14 Last updated: 2017-12-05Bibliographically approved

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