Fault Detection in Mobile Networks Using Diffusion Models
On the map: Diffusion models → Flow matching, time series and science
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.
Key facts. No citing papers in the map yet (data through 7 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.
Builds on
16 of 19 references.
Closest ancestors · 6
Same direction, newest first
- 2023Time Series Anomaly Detection using Diffusion-based Models2 Nov
- 2023DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection13 Oct
- 2023DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting3 Jun
- 2023On Diffusion Modeling for Anomaly Detection29 May
- 2023Diffusion Models for Time Series Applications: A Survey1 May
- 2022TabDDPM: Modelling Tabular Data with Diffusion Models30 Sep
Other references · 10
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
- 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
- 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
- 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
- 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
- 2023Large Generative AI Models for Telecom: The Next Big Thing?cited by 52
- 2017Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networkscited by 40
- 2023Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networkscited by 5
- 2020On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Seriescited by 3
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Not cited yet.
Similar, not cited
Close in wording, no citation either way
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- 2024Anomaly detection using Diffusion-based methods