提出分层智能架构，优化卫星网络中模型部署的可行性。
AI Lifecycle-Aware Feasibility Framework for Split-RIC Orchestration in NTN O-RAN
- 设计地面-低轨-高轨分层控制，支持星上推理与数据缓存学习。
- 推导出生命周期能耗与延迟公式，评估不同链路条件下的性能。
- 为卫星智能决策提供可落地的部署建议，适合运营商参考。
将人工智能引入非地面网络受限于卫星系统尺寸、重量与功耗（SWaP）及馈电链路容量，直接影响O-RAN闭环控制与模型生命周期管理。本文研究通过分层RIC（Split-RIC）架构，在地面、低轨（LEO）与高轨（GEO）间分布O-RAN控制层级的可行性。比较三种部署方案：(i) 地面中心控制+遥测传输；(ii) 地面-低轨分层控制，支持星上推理与存储转发学习；(iii) 基于星间链路的高轨-低轨多层控制。针对每种方案，推导涵盖训练数据传输、模型分发与近实时推理的生命周期能耗与延迟闭式表达式。在馈电链路状态、模型复杂度与轨道间断性条件下进行数值敏感性分析，确定操作者可用的可行性区域，明确星上推理与非地面学习回路在物理上优于地面卸载的适用边界。
原文摘要 · Abstract (English)
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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