AmbientFM is a foundation model designed for ambient sensing using ubiquitous WiFi signals. It is pre‑trained on 9.2 million unlabeled Channel State Information samples collected over 439 days from 20 commercial devices in real homes, making it the largest real‑world WiFi CSI pre‑training corpus reported to date. The model learns transferable wireless representations through contrastive learning, masked reconstruction, and physics‑informed objectives, and with lightweight adaptation it can transfer to nine downstream tasks, achieving above 0.90 AUROC on all classification benchmarks and supporting dense spatial reconstruction.
By Guozhen Zhu, Yuqian Hu, Sakila Jayaweera, Weihang Gao, Wei-Hsiang Wang, Jiaxuan Zhang, Beibei Wang, Chenshu Wu, K. J. Ray Liu
arXiv:2607. 09727v1 Announce Type: cross Abstract: WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolated silos where models are tailored to specific hardware dialects, fixed environments, and narrow tasks.
By Jiayi Chen, Weiting Ou, Guangxu Zhu
CIG-MAE is a self‑supervised framework for WiFi‑based human action recognition that uses a cross‑modal masked autoencoder to reconstruct both amplitude and phase of Channel State Information. It introduces an adaptive, information‑guided masking strategy that focuses on high‑density time‑frequency regions and employs a Barlow Twins regularizer to align cross‑modal representations without negative samples. Experiments on three public datasets show that CIG‑MAE outperforms state‑of‑the‑art SSL methods and even surpasses a fully supervised baseline, highlighting its data efficiency, robustness, and generalization.
By Gang Liu, Yanling Hao, Yixuan Zou
The paper surveys wireless foundation models (WFMs), highlighting their role in learning reusable representations from large-scale wireless data for physical-layer tasks. It systematically reviews WFM design components—pretraining, backbone architectures, and downstream adaptation—and categorizes the literature into five task families: signal recognition and demodulation, channel representation learning, RF sensing and localization, beam management, and spectrum sensing and monitoring, including multi-task models. The analysis reveals that while WFMs show promise, evidence of transferability varies across tasks and evaluation settings, and differences in datasets, modalities, architectures, and distribution shifts hinder clear conclusions about effective design choices.
By Alonso M. Pacheco Huachaca, Juan J. Rodriguez Rodriguez, Ahmed Aboulfotouh, Nelson L. S. da Fonseca, Carlos A. Astudillo, Hatem Abou-Zeid
arXiv:2512.22143v2 Announce Type: replace-cross
Abstract: Existing Wi-Fi sensing systems rely on injecting high-rate probing packets to extract channel state information (CSI), leading to communicati...
By Gaofeng Dong, Kang Yang, Mani Srivastava
arXiv:2610.09559v1 Announce Type: new
Abstract: Wi-Fi sensing promises to turn the everyday wireless signals that already surround us into ubiquitous sensors for human sensing. However, a fundamental...
By Daniel Eckhoff, Hua Kang, Zhitang Chen, Jie Chuai