  Semantic communications can reduce the resource consumption by transmitting
task-related semantic information extracted from source messages. However, when
the source messages are utilized for various tasks, e.g., wireless sensing data
for localization and activities detection, semantic communication technique is
difficult to be implemented because of the increased processing complexity. In
this paper, we propose the inverse semantic communications as a new paradigm.
Instead of extracting semantic information from messages, we aim to encode the
task-related source messages into a hyper-source message for data transmission
or storage. Following this paradigm, we design an inverse semantic-aware
wireless sensing framework with three algorithms for data sampling,
reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised
decoding, respectively. Specifically, on the one hand, we propose a novel RIS
hardware design for encoding several signal spectrums into one MetaSpectrum. To
select the task-related signal spectrums for achieving efficient encoding, a
semantic hash sampling method is introduced. On the other hand, we propose a
self-supervised learning method for decoding the MetaSpectrums to obtain the
original signal spectrums. Using the sensing data collected from real-world, we
show that our framework can reduce the data volume by 95% compared to that
before encoding, without affecting the accomplishment of sensing tasks.
Moreover, compared with the typically used uniform sampling scheme, the
proposed semantic hash sampling scheme can achieve 67% lower mean squared error
in recovering the sensing parameters. In addition, experiment results
demonstrate that the amplitude response matrix of the RIS enables the
encryption of the sensing data.
