Abstract
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:
- Cycle-Adaptive Masking Strategy: Dynamically adjusts masking patterns based on channel periodicity characteristics to learn robust wireless representations.
- Map-as-Prompt Framework: 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.
Blogger's Review: The introduction of SigMap not only provides new insights into the advancement of wireless localization technology but also demonstrates how to utilize map information for cross-scenario adaptation. This multimodal integration approach will play a crucial role in future localization applications, especially as we face the critical challenges of improving localization accuracy in the 5G/6G era.