AI Lifecycle-Aware Feasibility Framework for Split-RIC Orchestration in NTN O-RAN
This addresses the challenge of AI deployment in satellite networks for telecom operators, but appears incremental as it builds on existing O-RAN and Split-RIC concepts.
This paper tackles the problem of integrating AI into Non-Terrestrial Networks (NTN) by studying the feasibility of distributing O-RAN control across ground, LEO, and GEO segments through a Split-RIC architecture. The result includes derived closed-form expressions for lifecycle energy and latency, and numerical analysis yields operator-relevant feasibility regions that delineate when on-board inference and non-terrestrial learning loops are preferable to terrestrial offloading.
Integrating Artificial Intelligence (AI) into Non-Terrestrial Networks (NTN) is constrained by the joint limits of satellite SWaP and feeder-link capacity, which directly impact O-RAN closed-loop control and model lifecycle management. This paper studies the feasibility of distributing the O-RAN control hierarchy across Ground, LEO, and GEO segments through a Split-RIC architecture. We compare three deployment scenarios: (i) ground-centric control with telemetry streaming, (ii) ground--LEO Split-RIC with on-board inference and store-and-forward learning, and (iii) GEO--LEO multi-layer control enabled by inter-satellite links. For each scenario, we derive closed-form expressions for lifecycle energy and lifecycle latency that account for training-data transfer, model dissemination, and near-real-time inference. Numerical sensitivity analysis over feeder-link conditions, model complexity, and orbital intermittency yields operator-relevant feasibility regions that delineate when on-board inference and non-terrestrial learning loops are physically preferable to terrestrial offloading.