Human Sensing at mmWave and Sub-THz: 

NIST Channel Measurement and Modeling Methodologies

Radio Access and Propagation Metrology Group

National Institute of Standards and Technology (NIST), USA
28 GHz Context-Aware Channel Sounder
Tri-System

• 1 dual-polarized TX phase-array antenna board (64 microstrip antennas), synthesized into quasi-omnidirectional V and H patterns

• 4 dual-polarized RX phased-array antenna boards (256 microstrip antennas) – “Tetris” configuration – have 5.2° EL and 3.7° AZ 

beamwidths

• Optical cable use for phase synchronization between TX and RX

• 2 GHz bandwidth

• 360° (AZ) camera

• 360° (AZ) lidar

Monostatic configuration

TX

Lidar

Camera

RX

RF
Switched Beamforming

Analog beamforming:

• 6-bit phase precision per antenna: coarse beams, limited tapering, cannot compensate for hardware non-idealities

• Takes 3.5 μs to write to each antenna register to change phase

• Takes 3.5 μs x 64 antennas ≈ 0.23 ms to form a single beam

• Takes 4 s to sweep 1800 beams (finest angular precision)

Switched beamforming1:

• Effectively infinite phase precision: ideal beams, Kaiser tapering, can compensate for hardware non-idealities 

• Takes 3.5 μs to write to each antenna register to toggle on/off

• Takes 3.5 μs x 64 ≈ 0.23 ms to switch through all antennas

• Takes 0.23 m to sweep 1800 beams

1 Caudill, Derek, et al. "Real-time mmWave channel sounding through switched beamforming with 3-D dual-polarized phased-array antennas." IEEE Transactions on Microwave Theory 

and Techniques 69.11 (2021): 5021-5032.
RF Measurement Error

Switched beamforming gives quasi-ideal beams and 18 dB sidelobe 

suppression in AZ and EL domains with Kaiser taper

Pre-distortion filtering to de-embed RF fronts ends 

gives 47 dB sidelobe suppression in the delay domain

Domain error based on ground-truth LoS path:

accuracy

resolvability
Use Case: Gesture Recognition
Measurement Campaign

80 cases:

• 20 motions:

• 16 hand gestures (while sitting): 

• 4 hand motions (up, down, left, right)

• 2 hands moving simultaneously

• 4 body motions:

• Standing up

• Sitting down

• Standing still

• Sitting still

• 4 human subjects

• A: 168 cm, female

• B: 160 cm, female 

• C: 169 cm, male

• D: 179 cm, male

• Tri-system sampled in time at 2.6 ms over 3.9 s (1500 time samples)
MPCs from RF

• One RF time sample consists of 256 phase-synchronized 

complex CIRs captured in 0.5 ms

• The 256 CIRs are synthesized through the SAGE superresolution to extract discrete MPCs indexed in path gain, 

phase, delay, azimuth AoA, and elevation AoA

• Super-resolution algorithms nominally provide 5x the 

resolution of inherent beamwidth/beamwidth

• SAGE also de-embeds the microstrip antenna/ beam gain 

patterns so that the MPC properties reflect the channel alone 

(not the channel + the hardware)

MPCs extracted from one time

MPCs aggregated over time
Time Clustering

• MPCs are clustered jointly in the path gain, EL, AZ, delay, and time domains2

• Clustering is conventionally processed just in the path gain, EL, AZ, and/or delay domains

• Augmenting to the time domain not only increases resolvability and robustness, it also makes spatial/temporal 

consistency a byproduct of the clustering process

• Eight body parts can be consistently resolved over the 80 cases

2 Varshney, Neeraj, et al. "Quasi-deterministic channel propagation model for an urban environment at 28 GHz." IEEE Antennas and Wireless Propagation Letters 20.7 (2021): 

1145-1149.

MPCs aggregated over time MPCs clustered over time
RF Signature

160 cm, female 179 cm, male
Keypoints from Camera/Lidar

• 2D keypoints(EL, AZ) are extracted from camera samples

• 17 keypoints based on salient body features (hands, knees, shoulders, etc.)

• Machine-learning based algorithm

• Camera and lidar images are combined to convert 2D keypointsto 3D keypoints(EL, AZ, delay)

Camera time sample Lidar time sample

Extracted 3D keypoints
MPCs vs. Keypoints

MPCs clustered over time Keypoints clustered over time

Temporal and spatial (EL, AZ, delay) calibration in tri-system paramount!
Quasi-Deterministic Channel Model

Deterministic component 

given through keypoints:

Stochastic component given through residual 

between cluster centroid per and keypoint (per time):

Spatial/temporal consistency given as 

a Sum-of-Sinusoid (SoS) process:

Stochastic component and spatial/temporal 

consistency parameters can be body-part and/or 

human-subject specific (or simply aggregate)
Machine-Learning Based Applications

Channel modeling:

Classification:

keypoints MPCs

MPCs aggregated

over time

motion

class
Use Case: Gait Recognition
Measurement Campaign

Bistatic configuration 9 cases (bistatic angles) by moving TX
MPC Properties

3D view

Path gain (dB)

Path gain (dB) Path gain (dB)

2D (top) view

2D (side) view

Right limbs

Middle

Head

Torso and arms

Left limbs

Legs

4 periods of walk

Doppler spectrum
140 GHz Channel Sounder

• 28 GHz: 2 GHz bandwidth 

-> 140 GHz: 20 GHz bandwidth

• 28 GHz: 3.7 AZ beamwidth 

-> 0.74 AZ beamwidth

• Sign language recognition

Keysight M8199A AWG

140 GHz phased-array antennas 

(Rebeiz @ UCSD)

Keysight UXR0404AP digitizer