Data-Driven Radio Environment Map Estimation Using Graph Neural Networks

TL;DR

This paper introduces a novel method for estimating Radio Environment Maps (REMs) by leveraging Graph Neural Networks to utilize physical cell data and sparse signal measurements, improving spatial dependency modeling.

Contribution

The paper presents a new GNN-based approach for REM estimation that effectively captures spatial dependencies using network coverage graphs and sparse geo-located measurements.

Findings

Effective REM estimation using GNNs demonstrated.

Captures spatial dependencies better than traditional methods.

Utilizes sparse measurements for accurate mapping.

Abstract

Radio Environment Maps (REMs) are crucial for numerous applications in Telecom. The construction of accurate Radio Environment Maps (REMs) has become an important and challenging topic in recent decades. In this paper, we present a method to estimate REMs using Graph Neural Networks. This approach utilizes both physical cell information and sparse geo-located signal strength measurements to estimate REMs. The method first divides and encodes mobile network coverage areas into a graph. Then, it inputs sparse geo-located signal strength measurements, characterized by Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) metrics, into a Graph Neural Network Model to estimate REMs. The proposed architecture inherits the advantages of a Graph Neural Network to capture the spatial dependencies of network-wide coverage in contrast with network Radio Access Network node locations and spatial proximity of known measurements.