Graduation Semester and Year
Spring 2026
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical Engineering
First Advisor
Qilian Liang
Abstract
This dissertation investigates advanced methodologies in Array Signal Processing (ASP) and Machine Learning (ML) to enhance the performance, efficiency, and intelligence of next-generation wireless networks, with a primary focus on 5G and emerging 6G systems. As wireless networks face rapid traffic growth, increasingly heterogeneous service requirements, and more complex propagation environments, conventional design and optimization approaches become insufficient to meet evolving demands in reliability, capacity, spectral efficiency, and energy efficiency. On the network intelligence side, this work develops data-driven frameworks for causal discovery, scheduler enhancement, session-duration prediction, and Radio Resource Control (RRC) state optimization using real-world telecommunication network data. On the physical-layer side, it introduces rational non-integer antenna array methodologies for cost-effective and spectrally efficient wireless design, extending from conventional array deployments to massive Multiple-Input-Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA), semantic communication, and Kakeya-inspired spherical and movable-antenna architectures relevant to future 6G networks. Comprehensive analytical modeling and simulation results demonstrate that the proposed approaches improve capacity, spectral efficiency, semantic spectral efficiency, and communication-sensing tradeoff performance, while also reducing antenna count, deployment cost, and, in selected scenarios, energy consumption. Overall, the dissertation shows that integrating ML-driven network intelligence with rational array design provides a coherent and promising pathway for scalable, high-performance 5G/6G wireless systems.
Keywords
6G wireless networks, 5G, machine learning, massive MIMO, Rational Arrays, Non-integer Arrays, Radio Resource Control, Causal Discovery, Non-Orthogonal Multiple Access, Kakeya
Disciplines
Electrical and Electronics | Systems and Communications
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Polaganga, Roopesh Kumar, "ARRAY SIGNAL PROCESSING AND MACHINE LEARNING IN 5G/6G NETWORKS" (2026). Electrical Engineering Dissertations-Archive. 415.
https://mavmatrix.uta.edu/electricaleng_dissertations/415