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

arXiv: https://arxiv.org/abs/2405.00220 · HTML: https://mlsanity.com/paper/2405.00220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2110.01848/) (2021)
- [Deep Learning-based Signal Strength Prediction Using Geographical Images and Expert Knowledge](https://mlsanity.com/paper/2008.07747/) (2020)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [ML KPI Prediction in 5G and B5G Networks](https://mlsanity.com/paper/2404.01530/) (2024)

## Abstract (arXiv)

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.