用于无线定位的基础模型：预训练、适配与利用
Foundation Models for Wireless Localization: Pretraining, Adaptation, and Utilization
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中文总结 AI 辅助
针对现有无线定位方法的不足，本文提出基于基础模型的三阶段统一框架，通过大规模未标注数据学习可迁移表示，提升定位精度与跨环境泛化能力，并展望了相关研究方向。
中文摘要 AI 辅助
精准无线定位是6G网络的关键支撑技术，但在多样且快速变化的传播条件下仍面临挑战。基于模型的方法在多径信道不可分辨或出现模型失配时性能下降，而监督式深度学习需要大量标注数据集，且对新部署场景的泛化能力较差。受语言和视觉领域基础模型（Foundation Models, FMs）的启发，本文提出一种基于FM的无线定位统一框架，该框架从大规模未标注信道状态信息中学习可迁移的信道表示，并能以极少甚至无监督的方式适配新环境。本文回顾了FMs的基础原理，对比了FM范式与现有定位方法，介绍了包含大规模预训练、定位导向微调及上下文增强推理的三阶段框架，以及该框架可实现的感知位置应用。基于射线追踪的案例研究表明，该方法提升了定位精度和跨环境泛化能力。最后，本文展望了面向无线定位AI原生网络的关键研究方向。
英文摘要
Accurate wireless localization is a key enabler for 6G networks, yet remains challenging under diverse and rapidly changing propagation conditions. Model-based methods degrade when multipath channels are non-resolvable and model mismatches occur, while supervised deep learning demands large labeled datasets and generalizes poorly to new deployments. Inspired by foundation models (FMs) in language and vision, this article presents a unified framework for FM-based wireless localization that learns transferable channel representations from large-scale unlabeled channel state information and adapts to new environments with minimal or even no supervision. We review the fundamentals of FMs, compare the FM paradigm with existing localization approaches, and introduce a three-stage framework spanning large-scale pretraining, localization-oriented fine-tuning, and context-augmented inference, together with the location-aware applications it enables. Ray-tracing-based case studies show improved positioning accuracy and cross-environment generalization. Finally, we present an outlook on key research directions toward AI-native networks for wireless localization.
发表机构
- Chalmers University of Technology（查尔姆斯理工大学）
- The Hong Kong University of Science and Technology（香港科技大学）
- Shenzhen University（深圳大学）
- Uppsala University（乌普萨拉大学）
- National Sun Yat-sen University（国立中山大学）
- Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。
