Enabling intelligent nextG cellular networks through the Open RAN architecture

The 5th generation (5G) and beyond of cellular networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. However, achieving such fine-grained control over the Radio Access Network (RAN) is unfeasible with the current cellular architecture. To bridge this gap, the Open RAN paradigm and its specification introduce an ``open'' architecture with abstractions that facilitate closed-loop control and enable data-driven, intelligent optimization of the RAN at the user-level. This thesis focuses on designing and developing system-level solutions to enable intelligent control in the next generation of cellular networks through the Open RAN architecture. The main research areas explored in this thesis include (i) the design and evaluation of platforms for the creation, datasets generation and testing of the Open RAN architecture solutions; (ii) the development of Artificial Intelligence (AI)/Machine Learning (ML) models for various deployments and networking scenarios; and (iii) innovative methodologies for agile spectrum, infrastructure, and \gls{ai} management within Open RAN. Among the significant contributions of this thesis are ns-O-RAN, the first open-source simulation platform that integrates a functional 5G protocol stack in Network Simulator 3 (ns-3) with an O-RAN-compliant E2 interface, and the pioneering architectural design and implementation of the dApps, the real-time controllers for the O-RAN architecture. Furthermore, the solutions proposed in this thesis are leveraged to investigate various network optimization use cases deemed critical in cellular networks. The results demonstrate that our data-driven approaches outperform traditional Radio Resource Management (RRM) heuristics, enhancing overall RAN conditions at scale in both simulations and state-of-the-art experimental testbeds.