Analysis and Compensation of Power Amplifier Distortions in Wireless Communication Systems

Wireless communication devices transmit message signals which should possess desirable power levels for quality transmission. Power amplifiers are devices in the wireless transmitters which increase the power of signals to the desired levels, but produce nonlinear distortions due to their saturation property, resulting in degradation of the quality of the transmitted signal. This thesis talks about the analysis and performance of communication systems in presence of power amplifier nonlinear distortions. First, the thesis studies the effects of power amplifier nonlinear distortions on communication signals and proposes a simplified design for identification and compensation of the distortions at the receiver end of a wireless communication system using a two-step pilot signal approach. Step one involves the estimation of the channel state information of the wireless channel and step two estimates the power amplifier parameters. Then, the estimated power amplifier parameters are used for transmitter identification with the help of a testing procedure proposed in this thesis. With the evolution of millimeter wave wireless communication systems today, study and analysis of these systems is the need of the hour. Thus, the second part of this thesis is extended to study the performance of millimeter wave wireless communication systems in presence of power amplifier nonlinear distortions and derives an analytical expression for evaluation of the symbol error probability for this system. The proposed analysis evaluates the performance of millimeter wave systems theoretically without the need of simulations, and is helpful in studying systems in the absence of actual hardware

Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods

The effects of soil-foundation-structure (SFS) interaction and extreme loading on structural behaviors are important issues in structural dynamics. System identification is an important technique to characterize linear and nonlinear dynamic structures. The identification methods are usually classified into the parametric and non-parametric approaches based on how to model dynamic systems. The objective of this study is to characterize the dynamic behaviors of two realistic civil engineering structures in SFS configuration and subjected to impact loading by comparing different parametric and non-parametric identification results. First, SFS building models were studied to investigate the effects of the foundation types on the structural behaviors under seismic excitation. Three foundation types were tested including the fixed, pile and box foundations on a hydraulic shake table, and the dynamic responses of the SFS systems were measured with the instrumented sensing devices. Parametric modal analysis methods, including NExT-ERA, DSSI, and SSI, were studied as linear identification methods whose governing equations were modeled based on linear equations of motion. NExT-ERA, DSSI, and SSI were used to analyze earthquake-induced damage effects on the global behavior of the superstructures for different foundation types. MRFM was also studied to characterize the nonlinear behavior of the superstructure during the seismic events. MRFM is a nonlinear non-parametric identification method which has advantages to characterized local nonlinear behaviors using the interstory stiffness and damping phase diagrams. The major findings from the SFS study are: *The investigated modal analysis methods identified the linearized version of the model behavior. The change of global structural behavior induced by the seismic damage could be quantified through the modal parameter identification. The foundation types also affected the identification results due to different SFS interactions. The identification accuracy was reduced as the nonlinear effects due to damage increased. *MRFM could characterize the nonlinear behavior of the interstory restoring forces. The localized damage could be quantified by measuring dissipated energy of each floor. The most severe damage in the superstructure was observed with the fixed foundation. Second, the responses of a full-scale suspension bridge in a ship-bridge collision accident were analyzed to characterize the dynamic properties of the bridge. Three parametric and non-parametric identification methods, NExT-ERA, PCA and ICA were used to process the bridge response data to evaluate the performance of mode decomposition of these methods for traffic, no-traffic, and collision loading conditions. The PCA and ICA identification results were compared with those of NExT-ERA method for different excitation, response types, system damping and sensor spatial resolution. The major findings from the ship-bridge collision study include: *PCA was able to characterize the mode shapes and modal coordinates for velocity and displacement responses. The results using the acceleration were less accurate. The inter-channel correlation and sensor spatial resolution had significant effects on the mode decomposition accuracy. *ICA showed the lowest performance in this mode decomposition study. It was observed that the excitation type and system characteristics significantly affected the ICA accuracy

Time-varying model identification for time-frequency feature extraction from EEG data

A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (ARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine the model parameter estimates of the time-varying ARX model. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. Simulation studies and applications to real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of nonstationary processes