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.

翻译：将人工智能（AI）集成到非地面网络（NTN）中受到卫星SWaP和馈线链路容量联合限制的制约，这直接影响O-RAN闭环控制和模型生命周期管理。本文研究了通过Split-RIC架构将O-RAN控制层级分布到地面、低地球轨道（LEO）和地球静止轨道（GEO）各段的可行性。我们比较了三种部署场景：（i）基于地面控制与遥测流传输，（ii）地面-LEO Split-RIC架构下搭载板载推理与存储转发学习，（iii）借助星间链路实现的GEO-LEO多层控制。针对每种场景，我们推导出考虑训练数据传输、模型分发和近实时推理的生命周期能量与生命周期时延的闭式表达式。针对馈线链路条件、模型复杂度和轨道间歇性进行的数值敏感性分析，给出了运营商相关的可行性区域，明确了在何种条件下板载推理和非地面学习循环在物理上优于地面卸载方案。