Integrating Sensing and Communication in Cellular Networks via NR Sidelink

Salami, Dariush and Hasibi, Ramin and Savazzi, Stefano and Michoel, Tom and Sigg, Stephan

Abstract

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 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.

Bibtex

@article{salami2021integrating,
  title = {Integrating Sensing and Communication in Cellular Networks via NR Sidelink},
  author = {Salami, Dariush and Hasibi, Ramin and Savazzi, Stefano and Michoel, Tom and Sigg, Stephan},
  journal = {arXiv preprint arXiv:2109.07253},
  year = {2021},
  link = {https://arxiv.org/abs/2109.07253}
}