# Jamming Detection in Cell-Free MIMO with Dynamic Graphs

arXiv: https://arxiv.org/abs/2601.06075 · HTML: https://mlsanity.com/paper/2601.06075/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](https://mlsanity.com/paper/2005.07496/) (2020)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines](https://mlsanity.com/paper/2405.09564/) (2024)
- [One-Class Classification as GLRT for Jamming Detection in Private 5G Networks](https://mlsanity.com/paper/2405.09565/) (2024)
- [Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised Learning](https://mlsanity.com/paper/2405.10331/) (2024)

## Abstract (arXiv)

Jamming attacks pose a critical threat to wireless networks, particularly in cell-free massive MIMO systems, where distributed access points and user equipment (UE) create complex, time-varying topologies. This paper proposes a novel jamming detection framework leveraging dynamic graphs and graph convolutional neural networks (GCN) to address this challenge. By modeling the network as a dynamic graph, we capture evolving communication links and detect jamming attacks as anomalies in the graph evolution. A GCN-Transformer-based model, trained with supervised learning, learns graph embeddings to identify malicious interference. Performance evaluation in simulated scenarios with moving UEs, varying jamming conditions and channel fadings, demonstrates the method's effectiveness, which is assessed through accuracy and F1 score metrics, achieving promising results for effective jamming detection.