AM-FM: A Foundation Model for Ambient Intelligence Through WiFi
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917269427191808 |
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| author | Zhu, Guozhen Hu, Yuqian Jayaweera, Sakila Gao, Weihang Wang, Wei-Hsiang Zhang, Jiaxuan Wang, Beibei Wu, Chenshu Liu, K. J. Ray |
| author_facet | Zhu, Guozhen Hu, Yuqian Jayaweera, Sakila Gao, Weihang Wang, Wei-Hsiang Zhang, Jiaxuan Wang, Beibei Wu, Chenshu Liu, K. J. Ray |
| contents | Ambient intelligence, continuously understanding human presence, activity, and physiology in physical spaces, is fundamental to smart environments, health monitoring, and human-computer interaction. WiFi infrastructure provides a ubiquitous, always-on, privacy-preserving substrate for this capability across billions of IoT devices. Yet this potential remains largely untapped, as wireless sensing has typically relied on task-specific models that require substantial labeled data and limit practical deployment. We present AM-FM, the first foundation model for ambient intelligence and sensing through WiFi. AM-FM is pre-trained on 9.2 million unlabeled Channel State Information (CSI) samples collected over 439 days from 20 commercial device types deployed worldwide, learning general-purpose representations via contrastive learning, masked reconstruction, and physics-informed objectives tailored to wireless signals. Evaluated on public benchmarks spanning nine downstream tasks, AM-FM shows strong cross-task performance with improved data efficiency, demonstrating that foundation models can enable scalable ambient intelligence using existing wireless infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11200 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AM-FM: A Foundation Model for Ambient Intelligence Through WiFi Zhu, Guozhen Hu, Yuqian Jayaweera, Sakila Gao, Weihang Wang, Wei-Hsiang Zhang, Jiaxuan Wang, Beibei Wu, Chenshu Liu, K. J. Ray Machine Learning Signal Processing Ambient intelligence, continuously understanding human presence, activity, and physiology in physical spaces, is fundamental to smart environments, health monitoring, and human-computer interaction. WiFi infrastructure provides a ubiquitous, always-on, privacy-preserving substrate for this capability across billions of IoT devices. Yet this potential remains largely untapped, as wireless sensing has typically relied on task-specific models that require substantial labeled data and limit practical deployment. We present AM-FM, the first foundation model for ambient intelligence and sensing through WiFi. AM-FM is pre-trained on 9.2 million unlabeled Channel State Information (CSI) samples collected over 439 days from 20 commercial device types deployed worldwide, learning general-purpose representations via contrastive learning, masked reconstruction, and physics-informed objectives tailored to wireless signals. Evaluated on public benchmarks spanning nine downstream tasks, AM-FM shows strong cross-task performance with improved data efficiency, demonstrating that foundation models can enable scalable ambient intelligence using existing wireless infrastructure. |
| title | AM-FM: A Foundation Model for Ambient Intelligence Through WiFi |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2602.11200 |