Abstract
In this thesis, the focus is on addressing the challenges faced by Radio Access Networks (RANs) in adapting to dynamic demands without manual intervention. The Open RAN (O-RAN) architecture is introduced, which enables programmability, openness, virtualization, and disaggregation principles. The base station functions are implemented as Virtual Network Functions (VNF) and split across O-RAN nodes, including the Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU).
One of the main objectives of this thesis is to achieve load balance VNF splitting by intelligently distributing the workload across CUs and network links, thereby preventing network congestion and overload. This is addressed by proposing a heuristic algorithm. Additionally, Artificial Intelligence (AI)-based methods are employed to intelligently manage resource allocation for dynamic VNF splitting with different objectives. These objectives are robust VNF splitting to minimize frequent VNF reconfigurations, energy-efficient VNF splitting, and edge-AI empowered dynamic VNF splitting for network slicing. These objectives are formulated mathematically and incorporated into Deep Reinforcement Learning (DRL) and federated DRL frameworks, where reward functions are defined to guide the learning process.
The thesis presents significant contributions in proposing diverse O-RAN system designs, and evaluating the proposed solutions using abstract and real network topologies. The simulation results demonstrate that the heuristic solution effectively achieves load balance, with a small gap of ≤ 2% compared to optimal solutions for small network scales. Moreover, by fine-tuning the AI hyperparameters, the performance gap of the edge-AI enabled solution can be reduced by 3% compared to the optimal solution. The proposed solution for robust dynamic VNF splitting reduces the overhead of VNF reconfigurations by up to 76%, with a minor increase of up to 23% in computational costs. Additionally, the solution for energy-efficient VNF splitting achieves noteworthy energy savings, with up to 56% reduction compared to non-VNF splitting solutions.