Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks
This work addresses the challenge of enabling embedded intelligence and real-time analytics in NextG cellular networks for network operators, providing a proof-of-concept for autonomous and self-optimizing networks.
This paper explores the O-RAN architecture for NextG cellular networks, demonstrating the integration of O-RAN-compliant software with an open-source softwarized cellular network. Experiments on Colosseum show closed-loop integration of real-time analytics and control using deep reinforcement learning agents, and feasibility of RAN control via xApps to optimize scheduling policies for co-existing network slices.
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.