Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction
from Variable-Sized Maps
- URL: http://arxiv.org/abs/2310.04570v2
- Date: Tue, 10 Oct 2023 13:32:55 GMT
- Title: Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction
from Variable-Sized Maps
- Authors: Thomas M. Hehn, Tribhuvanesh Orekondy, Ori Shental, Arash Behboodi,
Juan Bucheli, Akash Doshi, June Namgoong, Taesang Yoo, Ashwin Sampath, Joseph
B. Soriaga
- Abstract summary: Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover.
We present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements.
- Score: 11.327456466796681
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Estimating path loss for a transmitter-receiver location is key to many
use-cases including network planning and handover. Machine learning has become
a popular tool to predict wireless channel properties based on map data. In
this work, we present a transformer-based neural network architecture that
enables predicting link-level properties from maps of various dimensions and
from sparse measurements. The map contains information about buildings and
foliage. The transformer model attends to the regions that are relevant for
path loss prediction and, therefore, scales efficiently to maps of different
size. Further, our approach works with continuous transmitter and receiver
coordinates without relying on discretization. In experiments, we show that the
proposed model is able to efficiently learn dominant path losses from sparse
training data and generalizes well when tested on novel maps.
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