Talks and Poster Presentations (with Proceedings-Entry):
E. Zöchmann, V. Va, M. Rupp, R. W. Heath:
"Geometric Tracking of Vehicular mmWave Channels to Enable Machine Learning of Onboard Sensors";
Talk: 2018 IEEE Globecom Workshops (GC Wkshps),
- 12-13-2018; in: "Proceedings of 2018 IEEE Globecom Workshops (GC Wkshps)",
Estimating time-selective millimeter wave wireless
channels with directive antennas poses a challenging task. A
feasible way of relaxing this channel estimation problem is to
focus on the tracking of a few multipath components (MPCs).
Aligning antenna beams to the tracked MPCs increases the
channel coherence time by several orders of magnitude. We
propose to track the MPCs geometrically. Our geometric trackers
are based on algorithms known as Doppler-bearing tracking.
We reformulate recent work on geometric-polar tracking into
an efficient recursive version. If the relative position of the
MPCs are known, other sensors on board of a vehicle, for
example, lidar, radar, camera, will be capable of performing
supervised learning based on their own observed data. Learning
the relationship between sensor data and MPCs allows onboard
sensors to participate in the channel tracking. Joint tracking
from many onboard sensors possibly increases the reliability of
the MPC tracking.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.