AbstractÐFuture sub-THz cellular deployments may be utilized to complement the coverage of the global positioning system (GPS) and provide centimeter-level accuracy. In this work, we use measurement data at 142 GHz to test a map-based position location algorithm in an outdoor urban microcell (UMi) environment. We utilize an extended Kalman filter (EKF) to track the position of the user equipment (UE) along a rectangular track, with the transmitter-receiver separation distances varying from 24.3 m to 52.8 m. The position and velocity of the UE are tracked by the EKF, with measurements of the angle of arrival and time of flight information obtained along an outdoor track, to provide a mean accuracy of 24.8 cm at 142 GHz, over 34 UE locations, using a single base station in line-of-sight and non-line-of-sight.
Millimeter Wave Position Location using Multipath Differentiation for 3GPP using Field Measurements
3GPP air interface standards support meter-level
position location of a user in a cellular network. With wider
bandwidths and narrow antenna beamwidths available at
mmWave frequencies, cellular networks now have the potential
to provide sub-meter position location for each user. In this
work, we provide an overview of 3GPP position location
techniques that are designed for line-of-sight propagation. We
discuss additional measurements required in the 3GPP standard
that enable multipath-based non-line-of-sight position location.
Further, we validate the concepts in this paper by using field
data to test a map-based position location algorithm in an indoor
office environment which has dimensions of 35 m by 65.5 m. We
demonstrate how the fusion of angle of arrival and time of flight
information in concert with a 3-D map of the office provides a
mean accuracy of 5.72 cm at 28 GHz and 6.29 cm at 140 GHz,
over 23 receiver distances ranging from 4.2 m to 32.3 m, using a
single base station in line-of-sight and non-line-of-sight. We also
conduct a theoretical analysis of the typical error experienced
in the map-based position location algorithm and show that the
complexity of the map-based algorithm is low enough to allow
real-time implementation.
- Publication Date:
- NSF-PAR ID:
- 10205765
- Journal Name:
- 2020 IEEE Global Communciations conference
- Volume:
- 2020
- Page Range or eLocation-ID:
- 1-7
- Sponsoring Org:
- National Science Foundation
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