Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps

TL;DR

This paper introduces a transformer-based neural network that predicts link-level path loss from variable-sized maps with buildings and foliage, working efficiently with sparse data and continuous coordinates.

Contribution

A novel transformer architecture for path loss prediction that handles variable map sizes, continuous coordinates, and sparse measurements, improving scalability and generalization.

Findings

Efficient learning of dominant path losses from sparse data

Good generalization to new, unseen maps

Scales well with different map sizes

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.