Integrating Sensing and Communication in Cellular Networks via NR Sidelink

TL;DR

This paper explores the integration of RF sensing with 5G NR sidelink communication, proposing methods for rotationally invariant gesture recognition using distributed processing and graph-based encoding.

Contribution

It introduces a novel approach for device-initiated sensing in beyond 5G networks, addressing angle dependence with rotational invariance techniques and multi-angle learning methods.

Findings

Proposed transformations achieve rotational invariance in mmWave point-cloud data.

Distributed processing with a graph encoder captures spatio-temporal features effectively.

Compared four multi-angle learning approaches on a new gesture dataset.

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 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.