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

TL;DR

This paper introduces an automated, AI-enabled testing framework for evaluating AI models in next-generation open RAN networks, addressing performance, security, and decision-making in realistic environments.

Contribution

It proposes a novel distributed testing framework with AI-driven decision exploration for O-RAN, supporting both simulation and hardware testing environments.

Findings

Framework enables comprehensive testing of AI models in O-RAN.

Supports rapid proof of concept and experimental research.

Leverages AI for intelligent decision space exploration.

Abstract

Openness and intelligence are two enabling features to be introduced in next generation wireless networks, e.g. Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms.