低轨（LEO）卫星网络中的双层空中联合学习：架构、关键技术与应用

中文摘要 AI 辅助

低轨（LEO）卫星网络正成为全球边缘智能的关键基础设施。在此背景下，将空中（OTA）计算与自适应波束跳变（BH）相结合，提供了一种创新框架，该框架无缝融合物理层模拟聚合与动态资源编排，有效克服了空间平台严苛的带宽和功率约束，同时将联合学习（FL）扩展到无处不在的物联网（IoT）部署中。本文首先概述双层OTA模型的基本原理，并介绍针对时变拓扑设计的自适应BH机制；随后总结该以学习为中心的架构的显著优势，包括将聚合延迟与设备密度解耦、优化时空资源效率、平衡数据新鲜度与信道质量。本文还探讨了多个应用场景，以突出该框架在不同垂直行业的潜力；此外，研究了一个具体案例，以证明所提调度策略的实际效能，结果显示其在基于卫星的FL系统的模型收敛速度和数据利用率方面取得了显著性能提升。最后，本文讨论了实施挑战并概述了未来研究方向，旨在为无处不在的非地面智能的演进提供见解。

英文摘要

Low Earth orbit (LEO) satellite networks are emerging as a pivotal infrastructure for global edge intelligence. In this context, integrating over-the-air (OTA) computation with adaptive beam hopping (BH) provides an innovative framework that seamlessly merges physical-layer analog aggregation with dynamic resource orchestration. This effectively overcomes the stringent bandwidth and power constraints of space platforms while extending federated learning (FL) to pervasive Internet-of-things (IoT) deployments. In this article, we first outline the fundamental principles of the dual-layer OTA model and introduce the adaptive BH mechanism designed for time-varying topologies. Then, we summarize the distinct advantages of this learning-centric architecture, which include decoupling aggregation latency from device density, optimizing spatio-temporal resource efficiency, and balancing data freshness with channel quality. Several application scenarios are explored to highlight the framework's potential across diverse vertical industries. Furthermore, a specific case is studied to demonstrate the practical efficacy of the proposed scheduling policy. The results reveal substantial performance gains in terms of model convergence speed and data utilization for satellite-based FL systems. Finally, we discuss the implementation challenges and outline future research directions, aiming to provide insights for the evolution of ubiquitous non-terrestrial intelligence.

发表机构

• Xi’an Jiaotong University（西安交通大学）
• School of Cyber Science and Engineering, Xi’an Jiaotong University（西安交通大学网络空间安全学院）
• Xidian University（西安电子科技大学）
• Beijing University of Posts and Telecommunications（北京邮电大学）
• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。