While Wi-Fi sensing offers a compelling, privacy-preserving alternative to cameras, its practical utility has been fundamentally undermined by a lack of robustness across domains. Models trained in one setup fail to generalize to new environments, hardware, or users, a critical "domain shift" problem exacerbated by modest, fragmented public datasets. We shift from this limited paradigm and apply a foundation model approach, leveraging Masked Autoencoding (MAE) style pretraining on the largest and most heterogeneous Wi-Fi CSI datasets collection assembled to date. Our study pretrains and evaluates models on over 1.3 million samples extracted from 14 datasets, collected using 4 distinct devices across the 2.4/5/6 GHz bands and bandwidths from 20 to 160 MHz. Our large-scale evaluation is the first to systematically disentangle the impacts of data diversity versus model capacity on cross-domain performance. The results establish scaling trends on Wi-Fi CSI sensing. First, our experiments show log-linear improvements in unseen domain performance as the amount of pretraining data increases, suggesting that data scale and diversity are key to domain generalization. Second, based on the current data volume, larger model can only provide marginal gains for cross-domain performance, indicating that data, rather than model capacity, is the current bottleneck for Wi-Fi sensing generalization. Finally, we conduct a series of cross-domain evaluations on human activity recognition, human gesture recognition and user identification tasks. The results show that the large-scale pretraining improves cross-domain accuracy ranging from 2.2% to 15.7%, compared to the supervised learning baseline. Overall, our findings provide insightful direction for designing future Wi-Fi sensing systems that can eventually be robust enough for real-world deployment.

翻译：尽管Wi-Fi感知提供了一种引人注目且保护隐私的摄像头替代方案，但其实际应用价值因跨域鲁棒性不足而受到根本性削弱。在特定配置下训练的模型无法泛化至新环境、硬件或用户，这一关键的“域偏移”问题因现有公开数据集规模有限且分散而进一步加剧。我们摒弃这一受限范式，采用基础模型方法，利用掩蔽自编码（MAE）风格预训练，基于迄今构建的最大规模、最异构的Wi-Fi信道状态信息（CSI）数据集集合进行学习。本研究在超过130万个样本上预训练并评估模型，这些样本提取自14个数据集，使用4种不同设备在2.4/5/6 GHz频段及20至160 MHz带宽范围内采集。我们的大规模评估首次系统性地解耦了数据多样性与模型容量对跨域性能的影响。结果确立了Wi-Fi CSI感知的尺度化趋势。首先，实验表明随着预训练数据量的增加，未见域性能呈现对数线性提升，这暗示数据规模与多样性是域泛化的关键。其次，基于当前数据体量，更大模型仅能为跨域性能带来边际增益，表明数据而非模型容量是当前Wi-Fi感知泛化的瓶颈。最后，我们在人体活动识别、手势识别和用户身份识别任务上进行了一系列跨域评估。结果表明，与监督学习基线相比，大规模预训练将跨域准确率提升了2.2%至15.7%。总体而言，我们的研究结果为设计未来能够足够鲁棒以适应实际部署的Wi-Fi感知系统提供了深刻的方向指引。