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Openness and intelligence are two enabling features to be introduced in nextgeneration wireless networks, e.g. Beyond 5G and 6G, to support serviceheterogeneity, open hardware, optimal resource utilization, and on-demandservice deployment. The open radio access network (O-RAN) is a promising RANarchitecture to achieve both openness and intelligence through virtualizednetwork elements and well-defined interfaces. While deploying artificialintelligence (AI) models is becoming easier in O-RAN, one significant challengethat has been long neglected is the comprehensive testing of their performancein realistic environments. This article presents a general automated,distributed and AI-enabled testing framework to test AI models deployed inO-RAN in terms of their decision-making performance, vulnerability andsecurity. This framework adopts a master-actor architecture to manage a numberof end devices for distributed testing. More importantly, it leverages AI toautomatically and intelligently explore the decision space of AI models inO-RAN. Both software simulation testing and software-defined radio hardwaretesting are supported, enabling rapid proof of concept research andexperimental research on wireless research platforms.

AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities