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Device-free wireless sensing has recently attracted significant interest dueto its potential to support a wide range of immersive human-machine interactiveapplications. However, data heterogeneity in wireless signals and data privacyregulation of distributed sensing have been considered as the major challengesthat hinder the wide applications of wireless sensing in large area networkingsystems. Motivated by the observation that signals recorded by wirelessreceivers are closely related to a set of physical-layer semantic features, inthis paper we propose a novel zero-shot wireless sensing solution that allowsmodels constructed in one or a limited number of locations to be directlytransferred to other locations without any labeled data. We develop a novelphysical-layer semantic-aware network (pSAN) framework to characterize thecorrelation between physical-layer semantic features and the sensing datadistributions across different receivers. We then propose a pSAN-basedzero-shot learning solution in which each receiver can obtain alocation-specific gesture recognition model by directly aggregating the alreadyconstructed models of other receivers. We theoretically prove that modelsobtained by our proposed solution can approach the optimal model withoutrequiring any local model training. Experimental results once again verify thatthe accuracy of models derived by our proposed solution matches that of themodels trained by the real labeled data based on supervised learning approach.

Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing