Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Electrical and Computer Engineering

Supervisor

Shami, Abdallah

2nd Supervisor

Al-Dweik, Arafat

Abstract

The thesis presents several detection and estimation techniques that can be incorporated into the fifth-generation (5G) networks. First, the thesis presents a novel system for orthogonal frequency division multiplexing (OFDM) to estimate the channel blindly. The system is based on modulating particular pairs of subcarriers using amplitude shift keying (ASK) and phase-shift keying (PSK) adjacent in the frequency domain, which enables the realization of a decision-directed (DD) one-shot blind channel estimator (OSBCE). The performance of the proposed estimator is evaluated in terms of the mean squared error (MSE), where an accurate analytical expression is derived and verified using Monte Carlo simulation under various channel conditions. The system has also extended to exploits the channel correlation over consecutive OFDM symbols to estimate the channel parameters blindly. Furthermore, a reliable and accurate approach has been introduced to evaluate the spectral efficiency of various communications systems. The metric takes into consideration the system dynamics, QoS requirements, and design constraints. Next, a novel efficient receiver design for wireless communication systems that incorporate OFDM transmission has been proposed. The proposed receiver does not require channel estimation or equalization to perform coherent data detection. Instead, channel estimation, equalization, and data detection are combined into a single operation, and hence, the detector performs a direct data detector (D3). The performance of the proposed D3 is thoroughly analyzed theoretically in terms of bit error rate (BER), where closed-form accurate approximations are derived for several cases of interest, and validated by Monte Carlo simulations. The computational complexity of D3 depends on the length of the sequence to be detected. Nevertheless, a significant complexity reduction can be achieved using the Viterbi algorithm (VA). Finally, the thesis proposes a low-complexity algorithm for detecting anomalies in industrial steelmaking furnaces operation. The algorithm utilizes the vibration measurements collected from several built-in sensors to compute the temporal correlation using the autocorrelation function (ACF). Furthermore, the proposed model parameters are tuned by solving multi-objective optimization using a genetic algorithm (GA). The proposed algorithm is tested using a practical dataset provided by an industrial steelmaking plant.

Summary for Lay Audience

In wireless communications systems, acquiring the knowledge of channel state information (CSI), commonly known as channel estimation (CE), and channel equalization are two fundamental tasks that a receiver device has to perform in order to extract the information symbols correctly. Generally speaking, the ultimate objective of designing CE methods is to maximize the data transmission reliability and data rate speed, while minimizing the complexity and processing delays. However, achieving all such conflicting objectives into one single design is generally not possible. Therefore, the system designer has to trade-off some of the objectives based on the availability of the resources and user experience requirements. In this study, we present a new method that can perform the CE process with less data loss and power requirements as compared to conventional methods. Furthermore, this technique does not impose additional computational complexity to the existing well-known estimators and can be implemented smoothly in the current fifth-generation (5G) of mobile networks. Besides, the thesis proposed a second receiver for wireless networks that can substantially reduce the power and the complexity of the current receivers with the advantage of the improved reliability. The proposed system was verified using probabilistic analysis and computer simulations over several environmental conditions. Furthermore, the thesis introduces a low-complexity algorithm for detecting anomalies in industrial steelmaking furnaces operation by using vibration sensory data. Anomalous data are usually seen as alarms or an alert flag for some problems such as credit card fraud, health issues, and server crashes. Therefore, the anomaly detection can be considered as an essential diagnosing tool for manufacturing that may create many business perspectives. The proposed algorithm is tested using a practical dataset provided by an industrial steelmaking plant.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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