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Introduction

Understanding the most efficient ways to enhance CSP operations remains a critical challenge for CSPs. This report looks at the advancements occurring in the field of AI, and how CSPs and vendors can take advantage of these developments to enhance network operations.

Highlights

  • How the CSP industry is looking to adopt advanced-AI capabilities to enhance operational inefficiencies.

Features and Benefits

  • Provides highlight on the evolution of machine-learning techniques.
  • Highlights how new techniques can help CSPs deliver automated network operations.

Key questions answered

  • How are CSPs using AI technologies such as machine learning?
  • What flaws are associated with some of the traditional machine-learning approaches and how can CSPs circumvent these flaws?
  • How are CSP vendors adopting the evolving machine-learning techniques to help CSPs enhance their operations?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

CSP look to advanced-AI techniques

  • Improving operational efficiency is a key CSP priority
  • AI enabled through traditional ML is not enough
  • DL is the next step
  • DRL takes AI a step further

Telecom vendors actively exploring DRL

  • Huawei uses DRL to automate network control
  • Packet Design adopts DRL in its Explorer SDN platform

Priorities to be considered when considering DRL

  • Quality data will be critical to CSPs adoption of DRL in network control
  • Investment in big data will be necessary

Appendix

  • Methodology
  • Further reading
  • Author

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