The indoor industrial environment can pose challenges to radio signals with obstructions
to line of sight and multiple reflections off numerous reflective surfaces or multipath. Good
non-line of sight positioning performance is then becoming a key differentiator for industrial
positioning techniques with warehouse asset tracking and automated guided vehicle navigation
being just a couple of applications among many. 5G is versatile not only for offering
a common connectivity and a precise positioning solution, but also for leveraging licensed, shared,
and unlicensed spectrum as well. We at Qualcomm Technologies now have two live OTA (over-the-air) testbeds for
indoor precise positioning in San Diego, with a new 60 Gigahertz millimeter wave unlicensed band testbed
complementing our upgraded sub-7 Gigahertz setup. As we work to evolve positioning for 5G Advanced
with 3GPP Release 18 and beyond, and with 6G, we look forward to cooperative positioning with
device-to-device communication using sidelink, and cooperative sensing to detect passive objects
and obstructions to achieve intelligent environment sensing. Those intelligent
environment sensing can be used to further enhance the wireless connectivity. We are now
ready to demonstrate our positioning performance for two 5G precise positioning techniques. One is machine
learning-assisted RF fingerprinting. The other is based on Time Difference of Arrival, or TDOA.
Machine learning is a key technology which complements 5G for robust precise positioning
in challenging indoor industrial environments. In this demo, we compare different methods for
precise 3D positioning in non-line of sight, or NLOS conditions, namely, 5G augmented with
machine learning for RF fingerprinting, and 5G augmented with an inertial sensor onboard the AGV
for time difference of arrival. Our live over-the-air testbed for sub-7 Gigahertz
in San Diego has six 5G transmission reception points, or TRPs, in 3.75 Gigahertz, a 5G test device
on board an automated guided vehicle, or AGV, and a positioning server. For the machine learning-
assisted RF fingerprinting method, we collected data from numerous locations within the testbed
with obstructed line of sight to map the 5G RF measurements to ground truth positions. This data
was then used to train a neural model which can translate these 5G RF measurements
to estimate positions. The charts show a significant gain in 3D positioning precision
by using machine learning for key intelligent positioning when compared to the reference case
with TDOA, which is why we believe machine learning is a key complementary technology for the future
of 5G precise positioning. We also simulated a large industrial deployment with raytracing and
sophisticated models for reflection, diffraction, and diffuse scattering. Our results show ML-enabled
positioning offers multi-fold enhancements to the positioning precision when compared to the
state-of-the-art classical positioning techniques even in harsh non-line of sight scenarios.
Precise positioning in 60 Gigahertz empowers industry and enterprise globally with the dual
benefit of better resolution in millimeter wave frequencies and ease of deployment
of a common connectivity and positioning solution in an unlicensed frequency band.
Our live over-the-air testbed for millimeter wave precise positioning here in San Diego
comprises of seven 5G transmission reception points, or TRPs, operating in 60 Gigahertz over
a 400 Megahertz bandwidth, a mobile platform with a 5G test device, and a co-located positioning engine.
The 60 Gigahertz TRPs regularly sweep a narrow beam for broad sector coverage with high signal
strength. The 5G test device mounted on the mobile platform receives positioning reference signals,
or PRS, from a subset of TRPs deployed in our testbed. This depends on
the mobile platform's location, and the direction of TRP and UE (user equipment) beams. A positioning engine
co-located with the 5G test device processes the PRS signals received from different TRPs
to estimate the position based on time difference of arrival, or TDOA. In summary, our demonstration
shows the feasibility of centimeter-level precise positioning for a mobile platform
in 60 Gigahertz millimeter wave unlicensed frequency band, for industrial IoT.