Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks

TL;DR

This paper demonstrates the integration of O-RAN architecture with data-driven control methods, including deep reinforcement learning, to enable autonomous, self-optimizing NextG cellular networks through real-time analytics and open interfaces.

Contribution

It presents the first large-scale integration of O-RAN-compliant software with an open-source cellular network and validates closed-loop control using deep reinforcement learning in a wireless network emulator.

Findings

Successful real-time analytics and control via deep reinforcement learning agents.

Feasibility of RAN control through xApps on the near real-time RIC.

Effective optimization of network slice scheduling policies.

Abstract

Next Generation (NextG) cellular networks will be natively cloud-based and built upon programmable, virtualized, and disaggregated architectures. The separation of control functions from the hardware fabric and the introduction of standardized control interfaces will enable the definition of custom closed-control loops, which will ultimately enable embedded intelligence and real-time analytics, thus effectively realizing the vision of autonomous and self-optimizing networks. This article explores the disaggregated network architecture proposed by the O-RAN Alliance as a key enabler of NextG networks. Within this architectural context, we discuss the potential, the challenges, and the limitations of data-driven optimization approaches to network control over different timescales. We also present the first large-scale integration of O-RAN-compliant software components with an open-source full-stack softwarized cellular network. Experiments conducted on Colosseum, the world's largest wireless network emulator, demonstrate closed-loop integration of real-time analytics and control through deep reinforcement learning agents. We also show the feasibility of Radio Access Network (RAN) control through xApps running on the near real-time RAN Intelligent Controller, to optimize the scheduling policies of co-existing network slices, leveraging the O-RAN open interfaces to collect data at the edge of the network.