AI-assisted Efficient Testing Methods for 5G Open RAN Radio, Distributed, and Central Units

The emergence of Open Radio Access Network (O-RAN) architecture in 5G networks has brought numerous benefits, including increased flexibility, scalability, and vendor diversity. However, the complexity of these networks and the diversity of their hardware/software vendors necessitate novel testing methodologies to ensure interoperability, performance compliance, and security of the hardware and software units from different vendors. Traditional testing methodologies struggle to cope with the heterogeneity and intricacy of O-RAN components, making it imperative to explore novel testing methods. AI-based testing methods bring significant advantages in addressing the challenges of testing 5G O-RAN hardware/software units.  By harnessing the power of AI, these methods can intelligently analyze vast amounts of data, identify corner/edge cases for testing, automate complex test cases, and visualize and generalize  test results.  In addition, Generative AI techniques can assist in generating comprehensive test cases for 5G O-RAN components, thereby accelerating test coverage speed and reducing manual effort. Embracing AI in testing 5G O-RAN components is essential for unlocking the full potential of this revolutionary technology and accelerating the deployment of 5G O-RAN systems to deliver seamless connectivity experiences to end-users. This project focuses on the research and development of AI-assisted methods for the testing of O-RAN's radio units (RU), distributed units (DU), and central units (CU) in terms of their interoperability, performance, and security.