  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.
