5G and beyond mobile networks will support heterogeneous use cases at an
unprecedented scale, thus demanding automated control and optimization of
network functionalities customized to the needs of individual users. Such
fine-grained control of the Radio Access Network (RAN) is not possible with the
current cellular architecture. To fill this gap, the Open RAN paradigm and its
specification introduce an open architecture with abstractions that enable
closed-loop control and provide data-driven, and intelligent optimization of
the RAN at the user level. This is obtained through custom RAN control
applications (i.e., xApps) deployed on near-real-time RAN Intelligent
Controller (near-RT RIC) at the edge of the network. Despite these premises, as
of today the research community lacks a sandbox to build data-driven xApps, and
create large-scale datasets for effective AI training. In this paper, we
address this by introducing ns-O-RAN, a software framework that integrates a
real-world, production-grade near-RT RIC with a 3GPP-based simulated
environment on ns-3, enabling the development of xApps and automated
large-scale data collection and testing of Deep Reinforcement Learning-driven
control policies for the optimization at the user-level. In addition, we
propose the first user-specific O-RAN Traffic Steering (TS) intelligent
handover framework. It uses Random Ensemble Mixture, combined with a
state-of-the-art Convolutional Neural Network architecture, to optimally assign
a serving base station to each user in the network. Our TS xApp, trained with
more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC
and controls its base stations. We evaluate the performance on a large-scale
deployment, showing that the xApp-based handover improves throughput and
spectral efficiency by an average of 50% over traditional handover heuristics,
with less mobility overhead.

本文介绍了 ns-O-RAN 软件框架，该框架整合了实际的生产级别的基于近实时 RAN 智能控制器 (near-RT RIC) 以及基于 ns-3 的 3GPP 模拟环境，以便开发 xApp 和自动化的大规模数据收集，并测试深度强化学习驱动的用户级优化控制策略。同时，作者提出了第一个面向用户的 O-RAN Traffic Steering (TS) 智能切换框架，将两种算法结合在一起，优化网络中的用户级别基站服务，使得传输速率和频谱效率平均提高了 50％。