# TelOps: AI-driven Operations and Maintenance for Telecommunication Networks

arXiv: https://arxiv.org/abs/2412.04731 · HTML: https://mlsanity.com/paper/2412.04731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems](https://mlsanity.com/paper/2109.06715/) (2021)

## Built on by (most cited first, 1 of 1)

- [From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)](https://mlsanity.com/paper/2605.20608/) (2026)

## Abstract (arXiv)

Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and maintenance (O&M) is extremely important to ensure the availability, effectiveness, and efficiency of TN communications. Different from the popular O&M technique for IT systems (e.g., the cloud), artificial intelligence for IT Operations (AIOps), O&M for TNs meets the following three fundamental challenges: topological dependence of network components, highly heterogeneous software, and restricted failure data. This article presents TelOps, the first AI-driven O&M framework for TNs, systematically enhanced with mechanism, data, and empirical knowledge. We provide a comprehensive comparison between TelOps and AIOps, and conduct a proof-of-concept case study on a typical O&M task (failure diagnosis) for a real industrial TN. As the first systematic AI-driven O&M framework for TNs, TelOps opens a new door to applying AI techniques to TN automation.