Date of Award
12-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering
Committee Chair/Advisor
Dr. Fatemeh Afghah
Committee Member
Dr. Kuang-Ching Wang
Committee Member
Dr. Linke Guo
Committee Member
Dr. Ismail Guvenc
Committee Member
Dr. Xiaoyong Yuan
Abstract
Next-generation wireless networks must deliver highly adaptive, scalable, and intelligent connectivity to satisfy the heterogeneous demands of emerging services, including enhanced mobile broadband, massive machine-type communications, and ultra reliable low latency applications. The Open Radio Access Network (O-RAN) paradigm has emerged as a key enabler of this vision, introducing openness, virtualization, and artificial intelligence (AI)-driven control into the RAN ecosystem. O-RAN’s disaggregated architecture facilitates multi-vendor interoperability and empowers intelligent management through the RAN Intelligent Controller (RIC). However, achieving real-time, autonomous, and generalized optimization in such a dynamic environment remains a significant challenge due to its distributed nature, non-stationary traffic, and diverse Quality of Service (QoS) requirements.
This dissertation addresses these challenges by developing a unified framework for AI-driven resource management and network slicing in O-RAN, centered on reinforcement learning (RL) and its advanced extensions. The research makes significant progress through several novel contributions, each addressing a fundamental limitation of conventional optimization and control methods. First, an Evolutionary Deep Reinforcement Learning (EDRL) framework is proposed to enhance the generalization and stability of O-RAN slice management by leveraging population-based exploration. The results demonstrate a substantial improvement, up to 62.2% in maximum cumulative reward, compared to baseline DRL models, highlighting the benefit of evolutionary diversity in dynamic spectrum and resource allocation. Building on this foundation, a Multi-Agent Reinforcement Learning (MARL) paradigm is developed to enable distributed intelligence among O-RAN components. By deploying cooperative agents across Distributed Units (DUs) and coordinating through the RIC, this approach achieves scalable, fault tolerant control that maintains QoS under fluctuating traffic patterns. To address traffic non-stationarity, the framework is extended with a hybrid LSTM-predictive DRL design that forecasts traffic loads and proactively reallocates resources to prevent QoS degradation.
To further enhance adaptability, a Meta-Deep Reinforcement Learning (Meta-DRL) approach is introduced, inspired by Model Agnostic Meta Learning (MAML), which enables rapid policy adaptation to unseen network conditions. This method significantly accelerates convergence and enhances robustness across varying O-RAN configurations. Additionally, to overcome the instability and poor generalization associated with sharp minima in non-convex optimization, a Sharpness Aware Reinforcement Learning (SAM-RL) framework is developed. By integrating Sharpness Aware Minimization into the policy optimization loop, the controller achieves flatter optima and more reliable performance across diverse environments. Finally, the research culminates in the introduction of LLM-Augmented Reinforcement Learning for O-RAN control, where Large Language Models (LLMs) are incorporated as reasoning and contextualization modules to guide decision making. Through frameworks such as ORAN-GUIDE and ORANSight-GPT, LLMs enable domain aware prompt generation and semantic understanding of network states, transforming the traditional numeric optimization pipeline into a cognitively guided decision process. This integration bridges symbolic reasoning with data driven control, enabling interpretable, adaptive, and generalized policy formation.
This dissertation establishes a comprehensive trajectory from distributed reinforcement learning to optimization aware and cognitively guided O-RAN intelligence. The proposed frameworks collectively advance the frontiers of autonomous network management by achieving improved convergence, interpretability, and generalization under real world non-stationary conditions. These contributions lay the groundwork for the evolution of fully autonomous, self optimizing, and self healing wireless networks envisioned in 6G and beyond.
Recommended Citation
Lotfi, Fatemeh, "Ai-Optimized Resource Management in Next-Gen Wireless Networks" (2025). All Dissertations. 4152.
https://open.clemson.edu/all_dissertations/4152
Author ORCID Identifier
https://orcid.org/0009-0009-1691-0029