Date of Award

8-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering

Committee Chair/Advisor

Dr. Fatemeh Afghah

Committee Member

Dr. Linke Guo

Committee Member

Dr. Tao Wei

Abstract

The Open Radio Access Network (O-RAN) framework enables unprecedented flexibility and optimization capabilities in 5G cellular networks by supporting modular, scalable deployments with slice-specific quality of service (QoS) management through embedded intelligent components. Even so, interference remains a persistent issue in dense 5G deployments, especially in mixed-vendor O- RAN environments where coordination between base stations is limited. This thesis discusses the evolution of cellular networks, types of wireless interference, and how recent developments have enabled learning-based approaches for network optimization. In addition, we specifically present an adaptive inter-cell interference mitigation xApp for the O-RAN near-real-time RAN Intelligent Controller (near-RT RIC) that performs coordinated physical resource block (PRB) allocation across multiple base stations under diverse traffic demands and channel conditions.

Unlike prior studies that rely primarily on simulation or fully hardware-centric testbeds, our solution is developed and evaluated in a full-stack O-RAN system built on srsRAN, Open5GS, and O-RAN Software Community (ORAN-SC), and deployed on a hybrid experimental platform that simultaneously combines software defined radio (SDR)-based and virtual gNodeBs (gNBs) and user equipment (UEs). This design preserves realistic PHY-layer interactions while substantially improving scalability, reproducibility, and cost-effectiveness for multi-cell interference experiments. We explicitly formulate inter-cell interference as a resource-control problem over shared PRB regions between neighboring cells and train a reinforcement learning (RL)-based model to learn coordinated allocation policies that adapt to per-user QoS demand and pathloss variation across the network. Experimental results show that our xApp improves QoS satisfaction and reduces interference-induced PRB loss relative to proportional-fair scheduling baselines while maintaining comparable aggregate network throughput. These results demonstrate the promise of scalable, learning-driven O-RAN control for practical interference management in heterogeneous multi-gNB 5G networks.

Author ORCID Identifier

0009-0008-6571-7439

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