This research report addresses how AI/ML systems can be seamlessly integrated into the next generation network architecture, ensuring compatibility and scalability across various network paradigms, including Open RAN, and how testing methodologies can be adapted to accommodate the intrinsic uncertainties associated with AI/ML algorithms in the network, while remaining adaptable for both traditional RAN and Open RAN configurations, as well as how the safety implications of AI integration in next generation networks can be addressed, ensuring that these technologies enhance user privacy, security, and trust. It concludes by discussing how predictive analytics and advanced testing techniques can be employed to proactively identify network bottlenecks, optimize resource allocation, and enhance overall network efficiency, regardless of the specific network infrastructure.
