Intelligence deployment in NTN-based O-RAN: Architectural options, practical constraints, and design guidelines

Non-Terrestrial Networks (NTNs) are expected to become an essential component of future sixth-generation (6G) mobile networks by extending coverage, improving service continuity, and supporting resilient connectivity beyond the reach of terrestrial infrastructures. At the same time, Open Radio Access Network (O-RAN) architectures provide functional disaggregation, open interfaces, and multi-time-scale control mechanisms that enable flexible and programmable RAN operation. Extending O-RAN principles to NTN environments, however, is not a direct transposition of terrestrial deployments. Space–ground propagation delay, intermittent feeder-link availability, limited onboard processing and energy resources, and the overhead of telemetry, control signaling, and model updates strongly constrain where RAN functions and Artificial Intelligence/Machine Learning (AI/ML) components can be placed. This paper analyzes intelligence deployment in NTN-based O-RAN architectures from a system-level and network-design perspective. We organize the design space by identifying representative deployment options for Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU) functions across terrestrial and non-terrestrial infrastructures. We then examine how O-RAN control entities and AI/ML workflows can be mapped onto these deployments, distinguishing between long-term orchestration, near-real-time control, and local inference. The discussion explicitly connects each deployment choice to practical feasibility constraints, including functional split requirements, RU–DU co-location, E2/O1 telemetry overhead, model and policy distribution traffic, onboard compute limitations, and intermittent connectivity. To support the architectural discussion, we provide an architecture-level control-loop feasibility analysis showing how NTN transport and processing constraints restrict the applicability of centralized, distributed, and hybrid intelligence placement strategies. The resulting analysis provides design guidelines for selecting suitable O-RAN and ML deployment configurations according to service requirements, control-loop time scales, and NTN infrastructure capabilities.