Spatial Transformers for Radio Map Estimation

TL;DR

This paper introduces STORM, an attention-based transformer model for radio map estimation that improves spatial resolution, reduces complexity, and enables active sensing for efficient measurement collection in cellular networks.

Contribution

The paper presents a novel transformer-based estimator for radio map estimation that outperforms existing methods and introduces an active sensing extension for optimized measurement collection.

Findings

Outperforms existing estimators in accuracy and efficiency

Exhibits lower computational complexity and full spatial resolution

Enables active sensing for optimized measurement collection

Abstract

Radio map estimation (RME) involves spatial interpolation of radio measurements to predict metrics such as the received signal strength at locations where no measurements were collected. The most popular estimators nowadays project the measurement locations to a regular grid and complete the resulting measurement tensor with a convolutional deep neural network. Unfortunately, these approaches suffer from poor spatial resolution and require a great number of parameters. The first contribution of this paper addresses these limitations by means of an attention-based estimator named Spatial TransfOrmer for Radio Map estimation (STORM). This scheme not only outperforms the existing estimators, but also exhibits lower computational complexity, translation equivariance, rotation equivariance, and full spatial resolution. The second contribution is an extended transformer architecture that allows STORM to perform active sensing, by which the next measurement location is selected based on the previous measurements. This is particularly useful for minimization of drive tests (MDT) in cellular networks, where operators request user equipment to collect measurements. Finally, STORM is extensively validated by experiments with one ray-tracing and two real-measurement datasets.