Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization

TL;DR - SigMap is a multimodal foundation model for wireless localization that combines learned signal representations with 3D geographic maps. It improves adaptation and zero-shot generalization across environments, potentially reducing reliance on scenario-specific labeled data.

• Uses cycle-adaptive masking based on wireless-channel periodicity to learn robust signal representations.
• Introduces “map-as-prompt,” encoding 3D geographic information as lightweight soft prompts for cross-scenario adaptation.
• Targets localization challenges in environment-sensitive 5G/6G applications.
• Reportedly achieves state-of-the-art results across multiple tasks and outperforms supervised and self-supervised baselines in unseen environments.