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A Genetic Algorithm for Joint Resource Allocation in Cooperative Cognitive Radio Networks

Yang, Wei; Ban, Dongsong; Liang, Weifa; Dou, Wenhua

Description

Existing literature in Cooperative Cognitive Radio Networks (CCRNs) always assumed a scenario where only one Primary User (PU) and several Secondary Users (SUs) coexist. However, in practice, multi-PUs and multi-SUs always coexist and the number of SUs is usually greater than that of PUs. Under such complex yet real scenarios, we assume that each PU not only allows a set of SUs to access its pre-allocated channel, but can leverage some of these SUs to improve its transmission rate via...[Show more]

dc.contributor.authorYang, Wei
dc.contributor.authorBan, Dongsong
dc.contributor.authorLiang, Weifa
dc.contributor.authorDou, Wenhua
dc.coverage.spatialIstanbul Turkey
dc.date.accessioned2015-12-10T22:51:22Z
dc.date.createdJuly 4-8 2011
dc.identifier.isbn9781424495399
dc.identifier.urihttp://hdl.handle.net/1885/59014
dc.description.abstractExisting literature in Cooperative Cognitive Radio Networks (CCRNs) always assumed a scenario where only one Primary User (PU) and several Secondary Users (SUs) coexist. However, in practice, multi-PUs and multi-SUs always coexist and the number of SUs is usually greater than that of PUs. Under such complex yet real scenarios, we assume that each PU not only allows a set of SUs to access its pre-allocated channel, but can leverage some of these SUs to improve its transmission rate via cooperative technologies. We consider a joint channel allocation and cooperation set partition problem in CCRNs, in which we aim to allocate a channel and assign a cooperation set that consists of several SUs for each PU, such that for a given period of time, the average transmission rates gained by all the users achieve maximum proportional fairness. We formulate the problem as a 0-1 non-linear programming model. Due to its NP-hardness, we propose a suboptimal Centralized Genetic Algorithm (CGA) for the problem. Extensive simulations demonstrate that CGA not only converges rapidly, but is shown to perform as well as 92% of the optimal solution delivered by brutal search, in terms of the fitness that reflects the fairness degree of the transmission performance gained by all the users.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesInternational Wireless Communications and Mobile Computing Conference (IWCMC 2011)
dc.sourceProceedings of the 7th International Wireless Communications and Mobile Computing Conference, IWCMC 2011
dc.source.urihttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5963770 10.1109/IWCMC.2011.5982411
dc.subjectKeywords: Channel allocation; Cognitive radio network; cooperation set partition; Extensive simulations; NP-hardness; Optimal solutions; Partition problem; Proportional fairness; Transmission performance; Transmission rates; Computer programming; Genetic algorithms channel allocation; cooperation set partition; cooperative cognitive radio networks
dc.titleA Genetic Algorithm for Joint Resource Allocation in Cooperative Cognitive Radio Networks
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2011
local.identifier.absfor080503 - Networking and Communications
local.identifier.absfor080201 - Analysis of Algorithms and Complexity
local.identifier.ariespublicationU3594520xPUB469
local.type.statusPublished Version
local.contributor.affiliationYang, Wei, National University of Defence Technology
local.contributor.affiliationBan, Dongsong, National University of Defense Technology
local.contributor.affiliationLiang, Weifa, College of Engineering and Computer Science, ANU
local.contributor.affiliationDou, Wenhua, National University of Defence Technology
local.description.embargo2037-12-31
local.bibliographicCitation.startpage167
local.bibliographicCitation.lastpage172
local.identifier.doi10.1109/IWCMC.2011.5982411
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-02-24T10:19:09Z
local.identifier.scopusID2-s2.0-80052515127
CollectionsANU Research Publications

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