Spectrum access in cognitive radio networks based on prediction and estimation

dc.contributor.authorDevanarayana, Chamara
dc.contributor.examiningcommitteeFapojuwo, Abraham (University of Calgary) Kordi, Behzad (Electrical and Computer Engineering) Peng, Qingjin (Mechanical Engineering) Diamond, Jeffrey (Electrical and Computer Engineering)en_US
dc.contributor.supervisorAlfa, Attahiru (Electrical and Computer Engineering)en_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractIn the literature, Cognitive radio (CR) as well as full-duplex (FD) communication technologies are proposed to increase the spectrum efficiency. The main contribution of this thesis is to introduce prediction and estimation techniques with low control overhead, and use the predicted or estimated information in resource allocation in CR networks, both in the overlay networks and the underlay networks. Prediction and estimation are important in increasing the data rate and keeping the interference at a low level. In the overlay scheme, I modeled the primary user (PU) traffic characteristics of the channels using the Probabilistic Suffix Tree (PST) algorithm. Then using this PST algorithm, I introduced a frequency hopping based control channel and derived its theoretical properties. Then I proposed two methods for selecting a channel set for transmission, which took into account both the PU channel usage statistics and, secondary user (SU) channel usage statistics as perceived by an SU of interest. The first scheme selected channels having the highest probability of successful transmission, while the second calculated a net reward using a marked Markov chain. Then using simulations, I showed that our scheme caused acceptable interference to the PUs and has better throughput performance, compared to a scheme selecting channels randomly. Then I proposed two joint channel assignment and power allocation schemes for a bi-directional FD underlay CR network with network assistance. The first scheme used the information on the number of total SU pairs present in the network. In the second scheme, I used least squares based estimation and Kalman filtering to estimate the interference at the monitoring stations using the local interference. It reduced the control overhead of keeping track of active SUs. In both of these schemes each SU pair decided on the channels to be used in the half-duplex mode and the full-duplex mode using local information. This joint optimization was done running channel assignment and power allocation algorithms alternatively. In the power allocation problem, I used a technique called monotonic optimization. After simulating both of these schemes I showed that the scheme based on estimation performs satisfactorily given that it has less control overhead.en_US
dc.description.noteOctober 2016en_US
dc.identifier.citationC. Devanarayana and A. Alfa, Proactive channel access in cognitive radio networks using statistical radio environment maps, EURASIP Journal on Wireless Communications and Networking, vol. 2015, no. 1, 2015. [Online]. Available: http://dx.doi.org/10.1186/s13638-015-0309-2en_US
dc.identifier.citationC. Devanarayana and A. S. Alfa, Predictive Channel Access in Cognitive Radio Networks based on Variable order Markov Models, in GLOBECOM2011, 2011 IEEE Global Telecommunications Conference, 2011en_US
dc.identifier.citationC. Devanarayana and A. S. Alfa, Proactive channel access in cognitive radio networks based on users statistics, in Cognitive Cellular Systems (CCS), 2014 1st International Workshop on, Sept 2014en_US
dc.identifier.citationC. Devanarayana and A. S. Alfa, Decentralized Channel Assignment and Power Allocation in a Full-Duplex Cognitive Radio Network, in Consumer Communications & Networking Conference (CCNC), 2016 13th IEEE Annual, Jan 2016.en_US
dc.publisherEURASIP Journal on Wireless Communications and Networkingen_US
dc.publisherIEEE Proceedingsen_US
dc.publisherIEEE Proceedingsen_US
dc.publisherIEEE Proceedingsen_US
dc.rightsopen accessen_US
dc.subjectCognitive Radioen_US
dc.subjectOverlay networksen_US
dc.subjectUnderlay networksen_US
dc.subjectPrediction and estimationen_US
dc.subjectProbabilistic suffix tree algorithmen_US
dc.subjectMonotonic optimizationen_US
dc.subjectResource allocationen_US
dc.titleSpectrum access in cognitive radio networks based on prediction and estimationen_US
dc.typedoctoral thesisen_US
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
1.18 MB
Adobe Portable Document Format
Main article
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
2.2 KB
Item-specific license agreed to upon submission