Generative AI improves automated testing of open radio access networks
The Role of Generative Artificial Intelligence in Testing and Automation of Open RAN Systems: GNG-OTA Framework
Cryptography and Security
Summary
Testing Open Radio Access Networks (O-RAN) is tough because the systems are made up of many parts from different makers, which need to work well together. The authors created a new framework called GNG-OTA that uses advanced AI methods like generative AI, language processing, and graph neural networks to help with automatic testing. This framework makes interoperability and performance testing more reliable, accurate, and efficient. It helps operators ensure that their customized networks function properly despite complexity and changing standards.
What this means in practice
- •For telecom network operators: Automatically test and verify interoperability and performance of multi-vendor O-RAN deployments using AI-driven automation.
- •For testing and quality assurance teams: Use AI-enhanced tools to improve efficiency and accuracy in validating O-RAN components against evolving network specifications.
Authors
Aly Sabri Abdalla, Vuk Marojevic
Abstract
Open Radio Access Networks (O-RAN) empower operators with choice and customization through their disaggregated and interoperable architecture. However, ensuring interoperability in O-RAN presents challenges due to complex testing environments and evolving specifications. This paper addresses these challenges by proposing the Generative AI and Graph Neural Network-based Open RAN Testing and Automation (GNG-OTA) framework. GNG-OTA utilizes state-of-the-art AI techniques including generative AI, natural language processing, and graph neural networks to enable accurate, reliable, efficient, and automatic O-RAN interoperability and performance testing.