  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.

Networking and Internet Architecture;Machine Learning