Advances in parameter estimation, source enumeration, and signal identification for wireless communications

Mohammadkarimi, Mostafa (2018) Advances in parameter estimation, source enumeration, and signal identification for wireless communications. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Parameter estimation and signal identification play an important role in modern wireless communication systems. In this thesis, we address different parameter estimation and signal identification problems in conjunction with the Internet of Things (IoT), cognitive radio systems, and high speed mobile communications. The focus of Chapter 2 of this thesis is to develop a new uplink multiple access (MA) scheme for the IoT in order to support ubiquitous massive uplink connectivity for devices with sporadic traffic pattern and short packet size. The proposed uplink MA scheme removes the Media Access Control (MAC) address through the signal identification algorithms which are employed at the gateway. The focus of Chapter 3 of this thesis is to develop different maximum Doppler spread (MDS) estimators in multiple-input multiple-output (MIMO) frequency-selective fading channel. The main idea behind the proposed estimators is to reduce the computational complexity while increasing system capacity. The focus of Chapter 4 and Chapter 5 of this thesis is to develop different antenna enumeration algorithms and signal-to-noise ratio (SNR) estimators in MIMO timevarying fading channels, respectively. The main idea is to develop low-complexity algorithms and estimators which are robust to channel impairments. The focus of Chapter 6 of this thesis is to develop a low-complexity space-time block codes (STBC)s identification algorithms for cognitive radio systems. The goal is to design an algorithm that is robust to time-frequency transmission impairments.

Item Type: Thesis (Doctoral (PhD))
Item ID: 13028
Additional Information: Includes bibliographical references (pages 196-221).
Keywords: Wireless communications, 5th Generation (5G), Internet of Things (IoT), Compressive sensing, multiple-input and multiple-output (MIMO) system, Space time block codes (STBCs), Frequency-selective fading channel, Machine learning, Parameter estimation, Time-varying fading channel, Kolmogorov–Smirnov test, Cramer-Rao Lower Bound (CRLB), Multiuser detection (MUD), maximum likelihood estimation (MLE), Method of moments, SNR estimation, Antenna enumeration, Doppler spread estimation
Department(s): Engineering and Applied Science, Faculty of
Date: May 2018
Date Type: Submission
Library of Congress Subject Heading: Wireless communication systems; Signal processing; Parameter estimation

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