Advanced interference management techniques for future wireless networks

In this thesis, we design advanced interference management techniques for future wireless networks under the availability of perfect and imperfect channel state information (CSI). We do so by considering a generalized imperfect CSI model where the variance of the channel estimation error depends on the signal-to-noise ratio (SNR). First, we analyze the performance of standard linear precoders, namely channel inversion (CI) and regularized CI (RCI), in downlink of cellular networks by deriving the received signal-to-interference-plus-noise ratio (SINR) of each user subject to both perfect and imperfect CSI. In this case, novel bounds on the asymptotic performance of linear precoders are derived, which determine howmuch accurate CSI should be to achieve a certain quality of service (QoS). By relying on the knowledge of error variance in advance, we propose an adaptive RCI technique to further improve the performance of standard RCI subject to CSI mismatch. We further consider transmit-power efficient design of wireless cellular networks. We propose two novel linear precoding techniques which can notably decrease the deployed power at transmit side in order to secure the same average output SINR at each user compared to standard linear precoders like CI and RCI. We also address a more sophisticated interference scenario, i.e., wireless interference networks, wherein each of the K transmitters communicates with its corresponding receiver while causing interference to the others. The most representative interference management technique in this case is interference alignment (IA). Unlike standard techniques like time division multiple access (TDMA) and frequency division multiple access (FDMA) where the achievable degrees of freedom (DoF) is one, with IA, the achievable DoF scales up with the number of users. Therefore, in this thesis, we quantify the asymptotic performance of IA under a generalized CSI mismatch model by deriving novel bounds on asymptotic mean loss in sum rate and the achievable DoF. We also propose novel least squares (LS) and minimum mean square error (MMSE) based IA techniques which are able to outperform standard IA schemes under perfect and imperfect CSI. Furthermore, we consider the implementation of IA in coordinated networks which enable us to decrease the number of deployed antennas in order to secure the same achievable DoF compared to standard IA techniques

Development of 2D- and 3D-BTEM for pattern recognition in higher-order spectroscopic and other data arrays

Ph.DDOCTOR OF PHILOSOPH

Journal of Telecommunications and Information Technology

kwartalni

Advanced Statistical Learning Techniques for High-Dimensional Imaging Data

With the rapid development of neuroimaging techniques, scientists are interested in identifying imaging biomarkers that are related to different subtypes or transitional stages of various cancers, neuropsychiatric diseases, and neurodegenerative diseases. Scalar-on-image models have been proven to demonstrate good performance in such tasks. However, due to their high dimensionality, traditional methods may not work well in the estimation of such models. Some existing penalization methods may improve the performance but fail to take the complex spatial structure of the neuroimaging data into account. In the past decade, the spatially regularized methods have been popular due to their good performance in terms of both estimation and prediction. Despite the progress, many challenges still remain. In particular, most existing image classification methods focus on binary classification and consequently may underperform for the tasks of classifying diseases with multiple subtypes or transitional stages. Moreover, neuroimaging data usually present significant heterogeneity across subjects. As a result, existing methods for homogeneous data may fail. In this dissertation, we investigate several new statistical learning techniques and propose a Spatial Multi-category Angle based Classifier (SMAC), a Subject Variant Scalar-on-Image Regression (SVSIR) model and a Masking Convolutional Neural Network (MCNN) model to address the above issues. Extensive simulation studies and practical applications in neuroscience are presented to demonstrate the effectiveness of our proposed methods.Doctor of Philosoph

Revisiting Allostery In Lac Repressor

Lac repressor (LacI) is an allosterically regulated transcription factor which controls expression of the lac operon in bacteria. LacI consists of a DNA-binding domain (DBD) and regulatory domain (RD), connected by a linker called the “hinge”. Binding of a small molecule inducer to the RD relieves repression through what is presumed to be a series of conformational changes mediated through the hinge. Despite decades of study, our understanding of this allosteric transition remains incomplete—mostly inferred from partial crystal structures and low-resolution scattering studies. In principle, solution-NMR could provide structural and dynamical information unobtainable by X-ray methods. However, due to LacI’s high molecular weight, low solubility, and transient stability, such studies have been limited to the non-allosteric, isolated DBD. Here, we present a solution-NMR study of the changes in structure and dynamics that underlie the allosteric transition of intact LacI. First, an optimized expression system is presented which enables characterization of LacI using NMR methodologies for high molecular weight proteins. Next, alternative NMR data sampling methods are implemented and further extended to overcome the low-solubility and transient stability limitations. Finally, these developments are combined to characterize LacI in each of its functional states. It is shown that the RD but not the DBD of apo LacI exists in an equilibrium between induced and repressed states with exchange occurring on the �s-ms timescale. Inducer binding in the absence of operator mostly quenches exchange but does not result in structural changes in the hinge or DBD. Conformational dynamics detected in the induced state are shown to be localized to a “network” of RD residues previously characterized to be critical for allostery. These dynamics are shown to be quenched in non-allosteric mutants which suggests functional relevance. Operator binding results in globally quenched dynamics and dramatic changes to the structure of the hinge. Inducer binding in the presence of operator results in only minor structural perturbation in the hinge and DBD. However, dynamics are shown to be activated in the RD. These results suggest that conformational dynamics may be critical to the allosteric transition of LacI