Multi Agent Team Learning in Disaggregated Virtualized Open Radio Access Networks (O-RAN)

TL;DR

This paper explores how multi-agent team learning can optimize disaggregated, virtualized O-RAN networks, addressing the complexity of autonomous control and coordination in modern wireless architectures.

Contribution

It introduces the application of multi-agent systems with team learning to enhance control and coordination in disaggregated O-RAN networks, filling a gap in existing SON solutions.

Findings

Proposes a case study on agent placement and AI feedback in O-RAN

Highlights challenges and open issues in multi-agent O-RAN control

Provides a roadmap for future research in this area

Abstract

Starting from the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments world wide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning to optimize the RAN in closed-loop, i.e. without human intervention, where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with team learning, can play an essential role in the control and coordination of controllers of O-RAN, i.e. near-real-time and non-real-time RAN Intelligent Controller (RIC). In this article, we first present the state-of-the-art research in multi-agent systems and team learning, then we provide an overview of the landscape in RAN disaggregation and virtualization, as well as O-RAN which emphasizes the open interfaces introduced by the O-RAN Alliance. We present a case study for agent placement and the AI feedback required in O-RAN, and finally, we identify challenges and open issues to provide a roadmap for researchers.