A Software Architecture for Seamless Simulated to Real Testbed Transition for O-RAN AI

Abstract

The growing interest in leveraging the potential of open interfaces and standards brought forth the Open Radio Access Network (O-RAN) specifications, which are becoming increasingly relevant in 5G and 6G Radio Access Networks (RANs). Amongst other features, O-RAN is expected to bring intelligence to 5G and 6G networks by enabling native support for Artificial Intelligence (AI) in the management of the RAN and its control plane. However, AI modules need to be previously trained and evaluated in simulated or emulated network testbeds as not to negatively affect users in real RANs. This separation between initial training and evaluation and the final usage of AI in real network deployments brings many challenges: while the O-RAN specification expects these AI modules to be reusable across RANs, their interfaces, timing, and data models are coupled with those of the RAN, requiring significant re-development effort to make the transition from simulation to real usage. To address this challenge, this work proposes the Open Intelligent Interfaces and Infrastructure Architecture (OI3A), a software architecture to decouple the specifics of RANs and simulators from the AI modules that are trained, evaluated, and used in them. OI3A is evaluated in a realistic proof-of-concept, which considers a use case of Deep Reinforcement Learning-driven MAC scheduling, allowing online training with minimal overhead.