  RF-sensing, the analysis and interpretation of movement or
environment-induced patterns in received electromagnetic signals, has been
actively investigated for more than a decade. Since electromagnetic signals,
through cellular communication systems, are omnipresent, RF sensing has the
potential to become a universal sensing mechanism with applications in smart
home, retail, localization, gesture recognition, intrusion detection, etc.
Specifically, existing cellular network installations might be dual-used for
both communication and sensing. Such communications and sensing convergence is
envisioned for future communication networks. We propose the use of NR-sidelink
direct device-to-device communication to achieve device-initiated,flexible
sensing capabilities in beyond 5G cellular communication systems. In this
article, we specifically investigate a common issue related to sidelink-based
RF-sensing, which is its angle and rotation dependence. In particular, we
discuss transformations of mmWave point-cloud data which achieve rotational
invariance, as well as distributed processing based on such rotational
invariant inputs, at angle and distance diverse devices. To process the
distributed data, we propose a graph based encoder to capture spatio-temporal
features of the data and propose four approaches for multi-angle learning. The
approaches are compared on a newly recorded and openly available dataset
comprising 15 subjects, performing 21 gestures which are recorded from 8
angles.
