提出新型模块化AI框架，解决6G三维网络跨域协同难题。
A Novel Compound AI Model for 6G Networks in 3D Continuum
- 将复杂任务分解为可互操作的专用模块，实现跨域智能协同。
- 支持动态拓扑适应与异构环境下的服务一致性保障。
- 适合研究6G网络智能化、分布式系统架构的学者与工程师。
3D连续体涵盖陆地、空中和空间域，6G网络是其关键使能技术。当前网络管理的AI方法依赖单一模型，难以捕捉跨域交互，缺乏适应性且计算开销巨大。本文提出一种形式化的复合AI系统模型，设计新颖的三元框架，将复杂任务分解为专业化、可互操作的模块。该模块化架构为应对6G在3D连续体中的独特挑战提供必要能力，要求异构组件在分布智能下协调运作。该方法引入模型与系统性能间的根本权衡，需谨慎处理。此外，我们识别出复合AI系统在3D连续体6G网络中面临的关键挑战，包括跨域资源编排、动态拓扑适应以及异构环境中一致的AI服务质量维持。
原文摘要 · Abstract (English)
The 3D continuum presents a complex environment that spans the terrestrial, aerial and space domains, with 6Gnetworks serving as a key enabling technology. Current AI approaches for network management rely on monolithic models that fail to capture cross-domain interactions, lack adaptability,and demand prohibitive computational resources. This paper presents a formal model of Compound AI systems, introducing a novel tripartite framework that decomposes complex tasks into specialized, interoperable modules. The proposed modular architecture provides essential capabilities to address the unique challenges of 6G networks in the 3D continuum, where heterogeneous components require coordinated, yet distributed, intelligence. This approach introduces a fundamental trade-off between model and system performance, which must be carefully addressed. Furthermore, we identify key challenges faced by Compound AI systems within 6G networks operating in the 3D continuum, including cross-domain resource orchestration, adaptation to dynamic topologies, and the maintenance of consistent AI service quality across heterogeneous environments.
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