Theses and Dissertations

Fall 2016

Feng, Chi

analysis of single-cell multi-user massive MIMO downlink. Perfect channel state information (CSI) is assumed at the BS and maximum ratio transmission (MRT) precoding scheme is adopted. We first investigate the distribution of the interference power and derive its probability density function (pdf) by

central limit theory. After that, analytical results on the outage probability and the sum-rate are derived. Different to existing work using the law of large numbers to derive the asymptotic deterministic signal-to-interference-plus-noise-ratio (SINR), the randomness of the interference in the SINR is

kept intact in our work, which allows the derivation of the outage probability. We further extend to networks with per-antenna power constraint. A modified MRT precoding scheme is proposed and the performance of the modified scheme is analyzed. Our work show that the modified MRT precoding can achieve

Fall 2014

Soliman, Samy Soliman Shokry Botros

developed to obtain novel, exact analytical expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the instantaneous end-to-end signal-to-noise ratio (SNR) of variable gain AF relaying systems operating over Rayleigh, Nakagami- extit{m} and Rician fading

framework for exact analysis of generic multihop cooperative relaying systems. This framework is valid for any modulation scheme, any fading channel distribution and any number of relays. The GTCF method is used in the thesis to obtain exact solutions for the ergodic capacity, outage probability and the

average symbol error probability of multihop AF relaying systems. A strength of the GTCF approach is that it can be used with tractable computational effort. The thesis shows the cases where the strength of the GTCF method is paramount, and identifies as well the cases where the use of the GTCF method

Fall 2018

Huang, Jiarao

conditional probability distribution of the state and energy is obtained based on the event-triggered information received at the remote estimator under the energy-dependent measurement transmission policy. The robust state estimation problems are investigated for linear Gaussian systems with event

resources of cyber-physical systems, two event-based state estimation problems are formulated and solved for systems described by hidden Markov models utilizing a new reference measure approach with the change of probability measure. For a linear Gaussian system with an energy harvesting sensor, the joint

-triggered scheduling and systems with unknown exogenous inputs utilizing the risk-sensitive approach, where closed-form risk-sensitive state estimates are derived. A fully distributed robust consensus-based filtering algorithm for systems measured by a sensor network is proposed with stability analysis on

Fall 2022

Ma,Chengran

detect the presence of FDIAs, a novel FDIA detection scheme is proposed in this thesis for three-phase distribution systems based on zero-sequence voltage (ZSV). From the voltage and power measurements, the bus voltages are estimated, and then the estimated ZSV is calculated as the sum of the estimated

bus voltages on the three phases to represent the degree of unbalance of the distribution system. Via mathematical analysis of the linear distribution system state estimation (DSSE) model, the distribution of the estimated ZSV under the normal condition is derived, based on which a whitening process

is adopted on the estimated ZSV to weaken the effect of measurement noises. The L2-norm of the whitened ZSV vector is then compared with a predefined threshold for the FDIA detection. Moreover, the probability of false alarm of the proposed scheme is derived, which can be utilized to determine the

Fall 2021

Kor, Yashar

Weather-related power outages in the distribution grid have a significant impact on the grid reliability - they impose a high cost on power utilities and considerable inconvenience to customers. Improvements in monitoring and data collection practices, as well as advanced data processing methods

based on Dempster-Shafer theory (DST), as well as Knowledge Graph-based representation of distribution grid topology (GridKG) suitable for integration of data characterizing different aspects of the distribution system. Three different architectures of a system for predicting types of weather-related

outages are proposed and evaluated. Weather and outage data are utilized for model development and evaluation of their performances. The developed system is capable of identifying the probability of outage occurrences with a focus on identifying outages caused by extreme wind, wet snow, and icing. An

Spring 2014

Duan, Ping

industrial process data. A new information theory-based distribution-free measure, transfer 0-entropy, is proposed for causality analysis based on the definitions of 0-entropy and 0-information without assuming a probability space. For the cases of more than two variables, a direct transfer 0-entropy concept

method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. A key assumption for the transfer entropy method is that the sampled data should follow a well-defined probability distribution; yet this assumption may not hold for all types of

detection plays a significant and central role. This thesis focuses mainly on information theory-based approaches for causality analysis that are suitable for both linear and nonlinear process relationships. Previous studies have shown that the transfer entropy approach is a very useful tool in quantifying

Fall 2018

Chen, Chunlin

any attempts on the theoretical analysis of the underlying point process model. In addition, extensions of the analytical methodology used in Poisson models to more general point process models are often hindered due to the lack of closed-form empty space function and the probability generating

studied the distributional properties of the empty space distances in the Matérn hard core point process of Type II, and proposed a piecewise probability density function for the empty space distance, including an exact expression and a heuristic formula, which can be fitted by aWeibull-like function

Stochastic geometry provides a way of defining and computing macroscopic properties of large scale wireless networks, by averaging over all possible spatial patterns of the network nodes. It abstracts the network as realizations of point process models, and analyzes the network performance in a

Fall 2015

Zhu, PeiCan

simulation. Studies of a simple p53-Mdm2 network reveal that random gene perturbation has a greater effect on the steady state distribution (SSD) compared to context switching activities. Secondly, stochastic multiple-valued networks (SMNs) are investigated to evaluate the effect of noise in a WNT5A network

Originally proposed in the 1960s, stochastic computation uses random binary bit streams to encode signal probabilities. Stochastic computation enables the implementation of basic arithmetic functions using simple logic elements. Here, the application of stochastic computation is extended to the

domain of gene network models and the fault-tree analysis of system reliability. Initially, context-sensitive stochastic Boolean networks (CSSBNs) are developed to model the effect of context sensitivity in a genetic network. A CSSBN allows for a tunable tradeoff between accuracy and efficiency in a

Spring 2014

Zarnani, Ashkan

employed in concert with fitting appropriate probability distributions to obtain statistical models that can dynamically provide PIs depending on the forecast context. Second, a range of quantile regression methods (including kernel quantile regression) are studied that can directly model the PI boundaries

arranged on a three-dimensional grid. However, there is always some level of error and uncertainty in the forecasts due to inaccuracies of initial conditions, the chaotic nature of weather, etc. Such uncertainty information is crucial in decision making and optimization processes involved in many

as a function of influential features. In the third class, we focus on various time series modeling approaches including heteroscedasticity modeling methods that can provide forecasts of conditional mean and conditional variance of the target for any forecast horizon. iv All presented PI computation

Spring 2019

Wei, Ziling

maximize the average throughput of the network, an optimal stopping strategy with threshold-based structure is derived in this scheme. To obtain the threshold, a low complexity algorithm is proposed to derive the stationary probability distribution of the energy level of each relay, and then, the threshold

challenges, with four research components.The first research component focuses on the optimal slot length configuration in cognitive radio networks. A slot length configuration scheme with imperfect spectrum sensing is proposed in this research. In the proposed scheme, the spectrum sensing result is

With the fast growth of mobile data traffic, spectrum scarcity has become a serious problem to the development of wireless networks. Due to the limited available spectrum resources, it is critical to improve the spectrum efficiency. Cognitive radio, opportunistic scheduling, and non-orthogonal