WIP: Impact of AI/ML Model Adaptation on RAN Control Loop Response Time

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models.

I. Introduction

Several consortia, such as Operator Defined Open and Intelligent Radio Access Networks (O-RAN), Telecom Infra Project (TIP), Open RAN Policy Coalition, to name a few, are promoting disaggregated architectures for the Radio Access Network (RAN) for 5G and beyond [1], [2]. These architectures are expected to bring many benefits such as enabling more market competition and customer choice, lower equipment costs, and improved network performance.