用地图当提示，让无线定位模型跨场景自适应。
Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
- 根据信道周期性动态调整掩码，提升信号表征鲁棒性。
- 零样本迁移性能超越现有方法，跨场景定位更准。
- 适合5G/6G智能驾驶、元宇宙等需要高精度定位的场景。
精准可靠的无线定位是5G/6G应用（如自动驾驶、扩展现实、智能制造）的关键支撑。然而，由于无线信号复杂且易受环境变化影响，跨场景精确定位仍具挑战。现有数据驱动方法泛化能力有限，需大量标注数据且难以适应新场景。为此，我们提出SigMap——一种多模态基础模型，包含两项创新：(1) 周期自适应掩码策略，根据信道周期特性动态调整掩码模式，以学习鲁棒的无线表征；(2) 创新的“地图即提示”框架，通过轻量级软提示整合3D地理信息，实现高效跨场景适应。大量实验表明，该模型在多个定位任务中达到当前最优性能，并在未见环境中展现出强零样本泛化能力，显著优于监督与自监督基线。
原文摘要 · Abstract (English)
Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing. Despite its importance, achieving precise localization across diverse environments remains challenging due to the complex nature of wireless signals and their sensitivity to environmental changes. Existing data-driven approaches often suffer from limited generalization capability, requiring extensive labeled data and struggling to adapt to new scenarios. To address these limitations, we propose SigMap, a multimodal foundation model that introduces two key innovations: (1) A cycle-adaptive masking strategy that dynamically adjusts masking patterns based on channel periodicity characteristics to learn robust wireless representations; (2) A novel "map-as-prompt" framework that integrates 3D geographic information through lightweight soft prompts for effective cross-scenario adaptation. Extensive experiments demonstrate that our model achieves state-of-the-art performance across multiple localization tasks while exhibiting strong zero-shot generalization in unseen environments, significantly outperforming both supervised and self-supervised baselines by considerable margins.
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