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

Daniele Tarchi

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.

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

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.
Paper Structure (64 sections, 15 equations, 9 figures, 3 tables)

This paper contains 64 sections, 15 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. O-RAN Fundamentals for Distributed Intelligence
  4. The Intelligent Controllers (RICs) and Applications
  5. Key Interfaces
  6. Orbital Regimes and Constraints
  7. Low Earth Orbit (LEO)
  8. Geostationary Orbit (GEO)
  9. Feeder Link Constraints
  10. The AI Lifecycle
  11. Training Phase
  12. Inference Phase
  13. The Fundamental Trade-offs
  14. Related Work and Gap Analysis
  15. O-RAN Integration in NTN
  16. ...and 49 more sections

Figures (9)

  • Figure 1: O-RAN Functional Architecture
  • Figure 2: Comparative O-RAN Deployment Architectures. (a) S1: Traditional Bent-Pipe approach with all intelligence grounded. (b) S2: Proposed Split-RIC with inference at the edge and training on the ground. (c) S3: Hierarchical approach with inference at the edge and training on a GEO hub via ISL.
  • Figure 3: Energy Feasibility Region: Impact of Data Volume
  • Figure 4: Impact of Channel Quality
  • Figure 5: Energy Feasibility Region: Impact of Complexity
  • ...and 4 more figures