Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. However, data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. Motivated by the observation that signals recorded by wireless receivers are closely related to a set of physical-layer semantic features, in this paper we propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data. We develop a novel physical-layer semantic-aware network (pSAN) framework to characterize the correlation between physical-layer semantic features and the sensing data distributions across different receivers. We then propose a pSAN-based zero-shot learning solution in which each receiver can obtain a location-specific gesture recognition model by directly aggregating the already constructed models of other receivers. We theoretically prove that models obtained by our proposed solution can approach the optimal model without requiring any local model training. Experimental results once again verify that the accuracy of models derived by our proposed solution matches that of the models trained by the real labeled data based on supervised learning approach.

翻译：非接触式无线感知因其在支持多种沉浸式人机交互应用中的潜力而近期引起了广泛关注。然而，无线信号的数据异质性和分布式感知的数据隐私法规被认为是阻碍无线感知在大型区域网络系统中广泛应用的主要挑战。基于无线接收器记录的信号与一组物理层语义特征密切相关的观察，本文提出了一种新颖的零样本无线感知解决方案，使得在一个或有限位置构建的模型能够直接迁移至其他位置，而无需任何标注数据。我们开发了一个新颖的物理层语义感知网络（pSAN）框架，用于表征物理层语义特征与不同接收器间感知数据分布之间的相关性。随后，我们提出了一种基于pSAN的零样本学习方法，通过该方法，每个接收器可以直接聚合其他接收器已构建的模型，从而获得位置特定的手势识别模型。我们从理论上证明，通过我们提出的解决方案获得的模型能够逼近最优模型，且无需进行任何本地模型训练。实验结果再次验证，基于我们提出的解决方案推导出的模型精度与基于监督学习使用真实标注数据训练的模型精度相匹配。