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Geometry-informed Channel Statistics Prediction Based upon Uncalibrated Digital Twins by Abouamer, Mahmoud Saad; Williams, Robin J.; Popovski, Petar is a scholarly article available to read on EtoBox.

What is Geometry-informed Channel Statistics Prediction Based upon Uncalibrated Digital Twins about?

Digital twins (DTs) of wireless environments can be utilized to predict the propagation channel and reduce the overhead of required to estimate the channel statistics. However, direct channel prediction requires data-intensive calibration of the DT to capture the environment properties relevant for propagation of electromagnetic signals. We introduce a framework that starts from a satellite image of the environment to produce an uncalibrated DT, which has no or imprecise information about the materials and their electromagnetic properties. The key idea is to use the uncalibrated DT to implicitly provide a geometric prior for the environment. This is utilized to inform a Gaussian process (GP), which permits the use of few channel measurements to attain an accurate prediction of the channel statistics. Additionally, the framework is able to quantify the uncertainty in channel statistics prediction and select rate in ultra-reliable low-latency communication (URLLC) that complies with statistical guarantees. The efficacy of the proposed geometry-informed GP is validated using experimental data obtained through a measurement campaign. Furthermore, the proposed prediction framework is sh

Author

Abouamer, Mahmoud Saad; Williams, Robin J.; Popovski, Petar

Published

2024

Language

EN