6G原生AI与信道基础模型
6G Native AI and Channel Foundation Models
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中文总结 AI 辅助
本文从系统设计视角界定6G原生AI的内涵，提出信道基础模型（CFMs）作为6G原生AI的技术范式，阐明其类型与优势，验证其在标注有限时可提升定位与波束预测性能。
中文摘要 AI 辅助
人工智能（AI）与无线通信的融合被广泛视为第六代（6G）系统的核心目标，但原生AI的含义以及应嵌入未来无线系统的AI能力类型仍待明确解读。本文从系统设计视角探讨6G原生AI，主张原生AI应作为无线系统的固有组件协同设计、优化与部署，而非部署后可移除的附加组件。从该视角看，传统特定任务的监督模型难以作为原生AI的主要技术基础，因为它们高度依赖标注数据，在不同传播条件下泛化性差，且需为不同信道相关任务设计碎片化方案。受这些局限性驱动，本文将信道基础模型（Channel Foundation Models, CFMs）定位为面向6G原生AI的以信道为中心的基础模型范式，界定CFMs的范围，阐明其与特定任务无线AI模型及大语言模型的差异，并总结三类预训练家族：生成式、判别式与混合式预训练。本文进一步探讨CFMs如何支持物理层处理、无线接入网智能及集成感知与通信，还纳入了基于CSI-CLIP的初步结果作为有限证据，表明当特定任务标注有限时，CFM式预训练可提升定位与波束预测性能。
英文摘要
The integration of artificial intelligence (AI) and wireless communications is widely regarded as a core objective of sixth-generation (6G) systems. However, both the meaning of native AI and the type of AI capability that should be embedded into future wireless systems remain open to interpretation. This paper discusses 6G native AI from a system-design perspective and argues that native AI should be co-designed, optimized, and deployed as an intrinsic component of the wireless system rather than as a removable post-deployment add-on. From this perspective, conventional task-specific supervised models are difficult to use as the main technical basis of native AI because they depend heavily on labeled data, generalize poorly across propagation conditions, and require fragmented designs for different channel-related tasks. Motivated by these limitations, we position channel foundation models (CFMs) as a channel-centric foundation-model paradigm for 6G native AI. We define the scope of CFMs, clarify their differences from task-specific wireless AI models and large language models, and summarize three pretraining families: generative, discriminative, and hybrid pretraining. We further discuss how CFMs may support physical-layer processing, radio access network intelligence, and integrated sensing and communications. Preliminary CSI-CLIP-based results are included as bounded evidence that CFM-style pretraining can improve positioning and beam prediction when task-specific labels are limited.
发表机构
- Xi’an Jiaotong-Liverpool University（西交利物浦大学）
- School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。
