http://hdl.handle.net/1974/758 Wed, 19 Jun 2013 18:52:29 GMT 2013-06-19T18:52:29Z http://hdl.handle.net/1974/8065 Title: Management and Sensing of Spectrum in Cognitive Radio Authors: Akhavan Astaneh, Saeed Abstract: Under the contemporary spectrum usage regulations, radio frequency bands are allocated statically to licensed users in a large geographical area and over a long period of time. Recent investigations have revealed that such static spectrum allocation has led to very poor usage of the overall spectrum. Cognitive radio has emerged as a new communication paradigm to improve the utilization of the radio spectrum. It is defined as an intelligent wireless communication system that allows coexistence of unlicensed users with the licensed ones as long as the perceived interference at the licensed user is capped below some acceptable level. In addition, the users in this system adopt efficient communication protocols to enhance spectral efficiency. We employ cooperative mechanisms wherein multiple users cooperate in order to accomplish the following tasks: 1) Cooperative spectrum sensing: In this task, the licensed users do not actively engage. Instead, the unlicensed users passively monitor the activity of the licensed users and transmit only during their absence. 2) Cooperative spectrum management: The licensed and unlicensed users can benefit from cooperation with each other, e.g., they can assist each other in transmission via relaying. In this fashion, they can save power or bandwidth and therefore, the whole network can accommodate more users. \end{itemize} In the first part of this thesis, we focus on cooperative spectrum sensing. We first study the performance of the optimal distributed detectors as the number of samples increases and identify the conditions under which the highest or lowest asymptotic performance is achieved. For each condition, we study several suboptimal detectors and obtain novel asymptotic expressions for their performance. We then consider distributed detection of an Orthogonal Frequency-Division Multiplexing (OFDM) signal source. We propose different optimal and suboptimal frequency-domain detectors and derive closed form expressions for their performance. These frequency-domain detectors, despite their lower computational complexity, outperform the state-of-the-art time-domain detectors. Finally, we consider distributed spectrum sensing in mixture-Nakagami fading channels. We propose several novel detectors that significantly outperform the traditional detectors. In all these cases, we prove that the suboptimal detectors are asymptotically optimal, i.e., their performance converges to the Uniformly Most Powerful (UMP) tests as the number of samples increases. In the second part of the thesis, we focus on cooperative spectrum management. We study the problem of cooperative relay selection and power allocation and determine the conditions, in terms of channel gains and network geometry, under which such cooperation leads to an increase in rate, or a reduction in power and bandwidth usage. Lastly, we propose cooperative protocols that exploit these results and greatly enhance spectrum efficiency. Description: Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2013-06-04 18:23:24.845 Tue, 04 Jun 2013 04:00:00 GMT http://hdl.handle.net/1974/8065 2013-06-04T04:00:00Z http://hdl.handle.net/1974/7796 Title: Marginal Models for Modeling Clustered Failure Time Data Authors: NIU, YI Abstract: Clustered failure time data often arise in biomedical and clinical studies where potential correlation among survival times is induced in a cluster. In this thesis, we develop a class of marginal models for right censored clustered failure time data and propose a novel generalized estimating equation approach in a likelihood-based context. We first investigate a semiparametric proportional hazards model for clustered survival data and derive the large sample properties of the regression estimators. The finite sample studies demonstrate that the good applicability of the proposed method as well as the substantial efficiency improvement in comparison with the existing marginal model for clustered survival data. Another important feature of failure time data we will consider in this thesis is a possible fraction of cured subjects. To accommodate the potential cure fraction, we consider a proportional hazards mixture cure model for clustered survival data with long-term survivors and develop a set of estimating equations by incorporating working correlation matrices in an EM algorithm. The dependence among the cure statuses and among the survival times of uncured patients within clusters are modeled by working correlation matrices in the estimating equations. For the parametric proportional hazards mixture cure model, we show that the estimators of the regression parameters and the parameter in the baseline hazard function are consistent and asymptotically normal with a sandwich covariance matrix that can be consistently estimated. A numerical study presents that the proposed estimation method is comparable with the existing parametric marginal method. We also extend the proposed generalized estimating equation approach to a semiparametric proportional hazards mixture cure model where the baseline survival function is nonparametrically specified. A bootstrap method is used to obtain the variances of the estimates. The proposed method is evaluated by a simulation study from which we observe a noticeable efficiency gain of the proposed method over the existing semiparametric marginal method for clustered failure time data with a cure fraction. Description: Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2013-01-30 21:23:48.968 Fri, 01 Feb 2013 05:00:00 GMT http://hdl.handle.net/1974/7796 2013-02-01T05:00:00Z http://hdl.handle.net/1974/7759 Title: Joint Modelling of Longitudinal Quality of Life Measurements and Survival Data in Cancer Clinical Trials Authors: Song, Hui Abstract: In cancer clinical trials, longitudinal Quality of Life (QoL) measurements on a patient may be analyzed by classical linear mixed models but some patients may drop out of study due to recurrence or death, which causes problems in the application of classical methods. Joint modelling of longitudinal QoL measurements and survival times may be employed to explain the dropout information of longitudinal QoL measurements, and provide more efficient estimation, especially when there is strong association between longitudinal measurements and survival times. Most joint models in the literature assumed classical linear mixed model for longitudinal measurements, and Cox's proportional hazards model for survival times. The linear mixed model with normal-distribution random effects may not be sufficient to model longitudinal QoL measurements. Moreover, with advances in medical research, long-term survivors may exist, which makes the proportional hazards assumption not suitable for survival times when some censoring times are due to potential cured patients. In this thesis, we propose new models to analyze longitudinal QoL measurements and survival times jointly. In the first part of this thesis, we develop a joint model which assumes a linear mixed tt model for longitudinal measurements and a promotion time cure model for survival data. We link these two models through a latent variable and develop a semiparametric inference procedure. The second part of this thesis considers a special feature of the QoL measurements. That is, they are constrained in an interval (0,1). We propose to take into account this feature by a simplex-distribution model for these QoL measurements. Classical proportional hazards and promotion time cure models are used separately to the situations, depending on whether a cure fraction is assumed in the data or not. In both cases, we characterize the correlation between the longitudinal measurements and survival times by a shared random effect, and derive a semiparametric penalized joint partial likelihood to estimate the parameters. The above proposed new joint models and estimation procedures are evaluated in simulation studies and applied to the QoL measurements and recurrence times from a clinical trial on women with early breast cancer. Description: Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2013-01-23 14:04:14.297 Wed, 23 Jan 2013 05:00:00 GMT http://hdl.handle.net/1974/7759 2013-01-23T05:00:00Z http://hdl.handle.net/1974/7684 Title: Networked Control Systems with Unbounded Noise under Information Constraints Authors: Johnston, Andrew Abstract: We investigate the stabilization of unstable multidimensional partially observed single-station, multi-sensor (single-controller) and multi-controller (single-sensor) linear systems controlled over discrete noiseless channels under fixed-rate information constraints. Stability is achieved under communication requirements that are asymptotically tight in the limit of large sampling periods. Through the use of similarity transforms, sampling and random-time drift conditions we obtain a coding and control policy leading to the existence of a unique invariant distribution and finite second moment for the sampled state. We use a vector stabilization scheme in which all modes of the linear system visit a compact set together infinitely often. Description: Thesis (Master, Mathematics & Statistics) -- Queen's University, 2012-12-06 15:06:37.449 Thu, 06 Dec 2012 05:00:00 GMT http://hdl.handle.net/1974/7684 2012-12-06T05:00:00Z