# Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network

arXiv: https://arxiv.org/abs/2406.04779 · HTML: https://mlsanity.com/paper/2406.04779/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Learning feed-forward one-shot learners](https://mlsanity.com/paper/1606.05233/) (2016)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks](https://mlsanity.com/paper/2011.11228/) (2020)
- [Siamese Graph Neural Networks for Data Integration](https://mlsanity.com/paper/2001.06543/) (2020)

## Built on by (most cited first, 3 of 3)

- [Telecom Foundation Models: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2408.03964/) (2024)
- [SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting](https://mlsanity.com/paper/2605.04530/) (2026)
- [Deep Learning on Graphs for Mobile Network Topology Generation](https://mlsanity.com/paper/2504.13991/) (2025)

## Abstract (arXiv)

There are vast number of configurable parameters in a Radio Access Telecom Network. A significant amount of these parameters is configured by Radio Node or cell based on their deployment setting. Traditional methods rely on domain knowledge for individual parameter configuration, often leading to sub-optimal results. To improve this, a framework using a Deep Generative Graph Neural Network (GNN) is proposed. It encodes the network into a graph, extracts subgraphs for each RAN node, and employs a Siamese GNN (S-GNN) to learn embeddings. The framework recommends configuration parameters for a multitude of parameters and detects misconfigurations, handling both network expansion and existing cell reconfiguration. Tested on real-world data, the model surpasses baselines, demonstrating accuracy, generalizability, and robustness against concept drift.