AM-FM：通过WiFi实现环境智能的基础模型
AmbientFM: A Foundation Model for Ambient Sensing
AI总结：
AM-FM通过WiFi实现环境智能的基础模型，利用大规模未标记数据预训练，提升跨任务性能和数据效率。
摘要：
环境智能，即持续理解物理空间中人类的存在、活动和生理状态，是智能环境、健康监测和人机交互的基础。WiFi基础设施为这一能力提供了无处不在、持续运行且隐私保护的子系统，适用于数十亿个物联网设备。然而，这一潜力仍未被充分利用，因为无线传感通常依赖于任务特定的模型，需要大量标记数据且限制了实际部署。我们提出了AM-FM，这是首个通过WiFi实现环境智能和传感的基础模型。AM-FM在9.2百万个未标记的信道状态信息（CSI）样本上进行预训练，这些样本来自全球20种商用设备类型，覆盖439天，通过对比学习、掩码重建和针对无线信号的物理信息目标学习通用表示。在涵盖九个下游任务的公共基准上评估，AM-FM表现出强大的跨任务性能，具有改进的数据效率，证明了基础模型可以利用现有无线基础设施实现可扩展的环境智能。
英文摘要：
Ambient intelligence seeks to continuously understand human presence, activity, and physiology in physical spaces, enabling smart environments, health monitoring, and human-computer interaction. WiFi infrastructure offers a ubiquitous, always-on, and privacy-preserving sensing substrate across billions of IoT devices. Yet ambient sensing with WiFi remains largely fragmented, with most systems relying on task-specific models, labeled data collection, and customized training pipelines. We present AmbientFM, a foundation model for ambient sensing through ubiquitous WiFi signals. AmbientFM is pre-trained on 9.2 million unlabeled Channel State Information (CSI) samples collected over 439 days from 20 commercial device types deployed in real homes, the largest real-world WiFi CSI pre-training corpus reported to date. It learns transferable wireless representations through contrastive learning, masked reconstruction, and physics-informed objectives tailored to wireless signals. With lightweight adaptation, a single backbone transfers to 9 downstream tasks, achieving above 0.90 AUROC on all classification benchmarks and supporting dense spatial reconstruction. AmbientFM reduces the need for labeled data and task-specific design, enabling scalable ambient intelligence on existing wireless infrastructure.
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