On spectrum sensing, resource allocation, and medium access control in cognitive radio networks

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dc.contributor.authorKaraputugala Gamacharige, Madushan Thilina
dc.contributor.examiningcommitteeYahampath, Pradeepa (Electrical and Computer Engineering) Irani, Pourang (Computer Science) Fapojuwo, Abraham (Electrical and Computer EIngineering, University of Calgary)en_US
dc.contributor.supervisorHossain, Ekram (Electrical and Computer Engineering)en_US
dc.date.accessioned2015-07-31T14:37:00Z
dc.date.available2015-07-31T14:37:00Z
dc.date.issued2015-05en_US
dc.date.issued2014-08en_US
dc.date.issued2013-11en_US
dc.date.issued2013-06en_US
dc.date.issued2012-12en_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractThe cognitive radio-based wireless networks have been proposed as a promising technology to improve the utilization of the radio spectrum through opportunistic spectrum access. In this context, the cognitive radios opportunistically access the spectrum which is licensed to primary users when the primary user transmission is detected to be absent. For opportunistic spectrum access, the cognitive radios should sense the radio environment and allocate the spectrum and power based on the sensing results. To this end, in this thesis, I develop a novel cooperative spectrum sensing scheme for cognitive radio networks (CRNs) based on machine learning techniques which are used for pattern classification. In this regard, unsupervised and supervised learning-based classification techniques are implemented for cooperative spectrum sensing. Secondly, I propose a novel joint channel and power allocation scheme for downlink transmission in cellular CRNs. I formulate the downlink resource allocation problem as a generalized spectral-footprint minimization problem. The channel assignment problem for secondary users is solved by applying a modified Hungarian algorithm while the power allocation subproblem is solved by using Lagrangian technique. Specifically, I propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. Finally, I propose a novel dynamic common control channel-based medium access control (MAC) protocol for CRNs. Specifically, unlike the traditional dedicated control channel-based MAC protocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for control information exchange.en_US
dc.description.noteOctober 2015en_US
dc.identifier.citationK. M. Thilina, and E. Hossain, ”Cognitive Radio Networks and Spectrum Sharing” book chapter in Transmission Techniques for Digital Communications (Eds. Sarah Kate Wilson and Stephen Wilson), Elsevier - to appearen_US
dc.identifier.citationK. M. Thilina, E. Hossain, and D.I. Kim, “DCCC-MAC: A Dynamic Common Control Channel-Based MAC Protocol for Cognitive Radio Networks,” IEEE Transactions on Vehicular Technology, to appear.en_US
dc.identifier.citationK.M. Thilina, H. Tabassum, E. Hossain, and D.I. Kim, ”Medium access control design for full duplex wireless systems: challenges and approaches,” IEEE Communications Magazine , vol.53, no.5, pp.112,120, May 2015en_US
dc.identifier.citationK. Thilina, E. Hossain, and M. Moghadari, “Cellular OFDMA Cognitive Radio Networks: Generalized Spectral Footprint Minimization,” IEEE Transactions on Vehicular Technology, August 2014en_US
dc.identifier.citationK. M. Thilina, K.W. Choi, N. Saquib, and E. Hossain, “Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE Journal on Selected Areas in Communications, vol. 31, no.11 pp. 2209–2221, November 2013en_US
dc.identifier.citationK. M. Thilina, M. Moghadari and E. Hossain, “Generalized Spectral Footprint Minimization for Multiuser Cognitive Radio Networks,” in Proc. IEEE Int. Conf. on Communication (ICC’13) , Sydney, Australia, 10-14 June 2013.en_US
dc.identifier.citationK. M. Thilina, K. W. Choi, N. Saquib, and E. Hossain, “Pattern Classification Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks: SVM and W-KNN Approaches,” in Proc. IEEE Global Communications Conference (Globecom’ 12), Anaheim, California, USA, 2012.en_US
dc.identifier.urihttp://hdl.handle.net/1993/30650
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.publisherIEEE Transactions on Vehicular Technologyen_US
dc.publisherIEEE Communications Magazineen_US
dc.publisherIEEE Transactions on Vehicular Technologyen_US
dc.publisherIEEE Journal on Selected Areas in Communicationsen_US
dc.publisherIEEE International Conference on Communication (ICC’13)en_US
dc.publisherIEEE Global Communications Conference (Globecom’ 12)en_US
dc.rightsopen accessen_US
dc.subjectCognitive radio networksen_US
dc.subjectResource allocationen_US
dc.subjectMedium access controlen_US
dc.subjectMAC protocolen_US
dc.subjectSpectrum sensingen_US
dc.subjectMachine learningen_US
dc.subjectDynamic common control channelen_US
dc.subjectSpectral footprint, Generalized spectral footprint minimizationen_US
dc.subjectpattern recognition, SVM, KNN, K-Meam, GMMen_US
dc.subjectOFDMAen_US
dc.subjectMultiuseren_US
dc.subjectcellularen_US
dc.titleOn spectrum sensing, resource allocation, and medium access control in cognitive radio networksen_US
dc.typedoctoral thesisen_US
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