AI Lifecycle-Aware Feasibility Framework for Split-RIC Orchestration in NTN O-RAN

• URL: http://arxiv.org/abs/2603.23252v1
• Date: Tue, 24 Mar 2026 14:18:03 GMT
• Title: AI Lifecycle-Aware Feasibility Framework for Split-RIC Orchestration in NTN O-RAN
• Abstract summary: We study the feasibility of distributing the O-RAN control hierarchy across Ground, LEO, and GEO segments through a Split-RIC architecture.<n>We derive closed-form expressions for lifecycle energy and lifecycle latency that account for training-data transfer, model dissemination, and near-real-time inference.
• Score: 3.1304584228602685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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