# Fault Detection in Mobile Networks Using Diffusion Models

arXiv: https://arxiv.org/abs/2404.09240 · HTML: https://mlsanity.com/paper/2404.09240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 16 of its 19 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting](https://mlsanity.com/paper/2306.01984/) (2023)
- [Diffusion Models for Time Series Applications: A Survey](https://mlsanity.com/paper/2305.00624/) (2023)
- [On Diffusion Modeling for Anomaly Detection](https://mlsanity.com/paper/2305.18593/) (2023)
- [On the Design Fundamentals of Diffusion Models: A Survey](https://mlsanity.com/paper/2306.04542/) (2023)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks](https://mlsanity.com/paper/1710.09207/) (2017)
- [Time Series Anomaly Detection using Diffusion-based Models](https://mlsanity.com/paper/2311.01452/) (2023)
- [DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2310.08800/) (2023)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series](https://mlsanity.com/paper/2010.08286/) (2020)

## Abstract (arXiv)

In today's hyper-connected world, ensuring the reliability of telecom networks becomes increasingly crucial. Telecom networks encompass numerous underlying and intertwined software and hardware components, each providing different functionalities. To ensure the stability of telecom networks, telecom software, and hardware vendors developed several methods to detect any aberrant behavior in telecom networks and enable instant feedback and alerts. These approaches, although powerful, struggle to generalize due to the unsteady nature of the software-intensive embedded system and the complexity and diversity of multi-standard mobile networks. In this paper, we present a system to detect anomalies in telecom networks using a generative AI model. We evaluate several strategies using diffusion models to train the model for anomaly detection using multivariate time-series data. The contributions of this paper are threefold: (i) A proposal of a framework for utilizing diffusion models for time-series anomaly detection in telecom networks, (ii) A proposal of a particular Diffusion model architecture that outperforms other state-of-the-art techniques, (iii) Experiments on a real-world dataset to demonstrate that our model effectively provides explainable results, exposing some of its limitations and suggesting future research avenues to enhance its capabilities further.