Fault Detection in Mobile Networks Using Diffusion Models

TL;DR

This paper introduces a novel diffusion model-based framework for anomaly detection in telecom networks, demonstrating improved performance and explainability on real-world multivariate time-series data.

Contribution

The paper proposes a new diffusion model architecture for telecom anomaly detection, outperforming existing methods and providing explainable results.

Findings

Diffusion models effectively detect anomalies in telecom networks.

The proposed architecture outperforms state-of-the-art techniques.

The model offers explainability and highlights areas for future improvement.

Abstract

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.