Context-Aware Mobile Network Performance Prediction Using Network & Remote Sensing Data

TL;DR

The paper addresses the challenge of accurately predicting telecom network performance in 5G environments where geography and urban structure heavily influence signal behavior. It proposes a remote-sensing‑driven forecasting framework that augments historical KPI time series with geospatial features derived from satellite imagery, and it introduces a geospatial profiling approach to cluster nodes for computational efficiency. Key contributions include a geospatially informed forecasting model, EuroSAT-based representation learning for robust cross-region generalization, and a demonstrated cold-start capability for new or planned sites, all aimed at enabling data-driven digital twins for networks. The approach offers practical benefits for network planning and performance estimation, particularly in rapidly urbanizing regions and during network densification.

Abstract

Accurate estimation of Network Performance is crucial for several tasks in telecom networks. Telecom networks regularly serve a vast number of radio nodes. Each radio node provides services to end-users in the associated coverage areas. The task of predicting Network Performance for telecom networks necessitates considering complex spatio-temporal interactions and incorporating geospatial information where the radio nodes are deployed. Instead of relying on historical data alone, our approach augments network historical performance datasets with satellite imagery data. Our comprehensive experiments, using real-world data collected from multiple different regions of an operational network, show that the model is robust and can generalize across different scenarios. The results indicate that the model, utilizing satellite imagery, performs very well across the tested regions. Additionally, the model demonstrates a robust approach to the cold-start problem, offering a promising alternative for initial performance estimation in newly deployed sites.