IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION, VOL. 5, NO. 3, JUNE 2024 557
Received 14 December 2023; revised 12 February 2024; accepted 25 February 2024. Date of publication 29 February 2024; date of current version 27 May 2024.
Digital Object Identifier 10.1109/OJAP.2024.3371834
Quasi-Deterministic Channel Propagation Model for
Human Sensing: Gesture Recognition Use Case
JACK CHUANG
1
, RAIED CAROMI
1
, JELENA SENIC
1
, SAMUEL BERWEGER
2
,
NEERAJ VARSHNEY
1,3
(Senior Member, IEEE), JIAN WANG
1
, CHIEHPING LAI
1
, ANURAAG BODI
1,3
,
WILLIAM SLOANE
1,4
, CAMILLO GENTILE
1
(Member, IEEE), AND NADA GOLMIE
2
(Fellow, IEEE)
1
Radio Access and Propagation Metrology Group, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
2
Communications Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
3
Prometheus Computing LLC, Cullowhee, NC 28723, USA
4
Electrical and Electronic Engineering Department, University of Canterbury, Christchurch 8041, New Zealand
CORRESPONDING AUTHOR: C. GENTILE (e-mail: camillo.gentile@nist.gov)
ABSTRACT We describe a quasi-deterministic channel propagation model for human gesture recognition
reduced from real-time measurements with our context-aware channel sounder, considering four human
subjects and 20 distinct body motions, for a total of 120000 channel acquisitions. The sounder
features a radio-frequency (RF) s ystem with 28 GHz phased-array antennas to extract discrete multipaths
backscattered from the body in path gain, delay, azimuth angle-of-arrival, and elevation angle-of-arrival
domains, and features camera / Lidar systems to extract discrete keypoints that correspond to salient
parts of the body in the same domains as the multipaths. Thanks to the precision of the RF system, with
average error of only 0.1 ns in delay and 0.2
◦
in angle, we can reliably associate the multipaths to the
keypoints. This enables modeling the backscatter properties of individual body parts, such as Radar cross-
section and correlation time. Once the model is reduced from the measurements, the channel is realized
through raytracing a stickman of keypoints – the deterministic component of the model to represent
generalizable motion – superimposed with a Sum-of-Sinusoids process – the stochastic component of the
model to render enhanced accuracy. Finally, the channel realizations are compared to the measurements,
substantiating the model’s high fidelity.
INDEX TERMS 6G, JCAS, camera, Lidar, joint communications and sensing, 28 GHz, phased-array
antennas.
I. INTRODUCTION
T
HE EVOLUTION of wireless networks from generation
to generation over the last 40 years has witnessed ever
wider bandwidths, ever more antennas, and ever shorter
packet durations, all for the sole purpose of delivering
higher communications throughput. As a byproduct, today’s
networks have fine enough resolution in the respective delay
(range), angle (space), and Doppler frequency shift (velocity)
domains to enable sensing the contours of relatively small
targets such as humans, vehicles, and robots by way of
Radar [1]. What is more, the pervasiveness of today’s
networks – composed from s mall cells, relays, Wi-Fi routers,
etc., in addition to just cell towers — enables ubiquitous
monitoring and tracking of targets for Internet of Things
(IoT) applications such as smart home, smart manufacturing,
smart transportation, and smart healthcare [2], [3], [4], [5].
The extension of network functionality from communications
alone to sensing has paved the way for joint communications
and sensing (JCAS) as the defining trait of 6G networks.
And, as their 5G predecessors expanded operation into the
millimeter-wave (mmWave) bands, 6G networks will expand
operation into the sub-Terahertz (sub-THz) bands, where
bandwidths are projected in the tens of Gigahertz, number
of antennas in the thousands, and packet durations in the
c
2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
