Open RAN (Radio Access Network) is revolutionizing the telecom space by introducing a framework based on the concepts of virtualization and openness. O-RAN fosters virtualized and disaggregated RAN components connected via open interfaces based on specifications by the O-RAN Alliance. The network is optimized using RAN intelligent controllers (RICs), which can take data-driven, closed-loop actions in a RAN built in a multi-vendor, interoperable environment. The goal of this paper is to provide insights and guidance about the paradigm shift brought by O-RAN in order to create open, softwarized, intelligent and optimized networks. We focus on the intelligence aspects by providing an in-depth view of the near-RT and non-RT RICs specified by the O-RAN Alliance, including the architecture and interfaces. A novel aspect of this paper is that we provide guidelines in terms of the artificial intelligence and machine learning (AI/ML) approaches and frameworks that are useful in the O-RAN context, and consider the applications (xApps and rApps) that can be created to programmatically and autonomously control and optimize the network through the RICs for V2X, Industry 5.0, and other very demanding service types. Additionally, we provide the E2E network slice orchestration architecture, and demonstrate the suitability of O-RAN for the requirements of the service types to be achieved. Finally, we discuss research challenges and opportunities and overview existing experimental research platforms that are used to innovate and drive advances in the O-RAN effort.
CITATION STYLE
Marinova, S., & Leon-Garcia, A. (2024). Intelligent O-RAN Beyond 5G: Architecture, Use Cases, Challenges, and Opportunities. IEEE Access, 12, 27088–27114. https://doi.org/10.1109/ACCESS.2024.3367289
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