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TelOps: AI-driven Operations and Maintenance for Telecommunication Networks

Yuqian Yang, Shusen Yang, Cong Zhao, Zongben Xu

TL;DR

The paper addresses how to perform operations and maintenance for Telecommunication Networks (TNs) using AI, proposing TelOps to overcome topological dependence, heterogeneous software, and limited failure data. TelOps introduces a layering architecture that integrates mechanism, data, and empirical knowledge with a knowledge-guided machine learning layer, and demonstrates its value through a case study on failure diagnosis in Mobile Access Networks (MANs) showing up to 28% higher accuracy at peak times compared to baselines. The authors contrast TelOps with AIOps to highlight the necessity of TN-specific knowledge embedding and data handling, and they discuss opportunities in systematic knowledge embedding, knowledge reuse, and resource provisioning for autonomous TNs. The work provides a concrete framework, architectural rationale, and empirical evidence that knowledge-augmented ML can improve O&M performance in complex, real-world TNs, paving the way for more scalable and autonomous telecom networks.

Abstract

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.

TelOps: AI-driven Operations and Maintenance for Telecommunication Networks

TL;DR

The paper addresses how to perform operations and maintenance for Telecommunication Networks (TNs) using AI, proposing TelOps to overcome topological dependence, heterogeneous software, and limited failure data. TelOps introduces a layering architecture that integrates mechanism, data, and empirical knowledge with a knowledge-guided machine learning layer, and demonstrates its value through a case study on failure diagnosis in Mobile Access Networks (MANs) showing up to 28% higher accuracy at peak times compared to baselines. The authors contrast TelOps with AIOps to highlight the necessity of TN-specific knowledge embedding and data handling, and they discuss opportunities in systematic knowledge embedding, knowledge reuse, and resource provisioning for autonomous TNs. The work provides a concrete framework, architectural rationale, and empirical evidence that knowledge-augmented ML can improve O&M performance in complex, real-world TNs, paving the way for more scalable and autonomous telecom networks.

Abstract

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.

Paper Structure

This paper contains 21 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. The Layering Architecture of TelOps
  3. TelOps Tasks at the Application Layer
  4. Preventive TelOps Tasks
  5. Reactive TelOps Tasks
  6. Supporting Layers of TelOps
  7. TelOps vs. AIOps
  8. Comparison of the Physical Layer
  9. Comparison of the Data Layer
  10. Comparison of the Knowledge Layer
  11. Comparison of the Machine Learning Layer
  12. Case study: Failure Diagnosis for Mobile Access Networks with TelOps
  13. The Failure Diagnosis Task for MANs
  14. Failure Diagnosis for MANs using TelOps
  15. Experimental Setups
  16. ...and 6 more sections

Figures (4)

  • Figure 1: The layering architecture of TelOps.
  • Figure 2: Representative Preventive and Reactive O&M Tasks for a Typical TN.
  • Figure 3: The workflow of failure diagnosis for MANs using TelOps.
  • Figure 4: The accuracy of failure root cause identification for the MAN.