The goal of Next-Generation Networks is to improve upon the current
networking paradigm, especially in providing higher data rates, near-real-time
latencies, and near-perfect quality of service. However, existing radio access
network (RAN) architectures lack sufficient flexibility and intelligence to
meet those demands. Open RAN (O-RAN) is a promising paradigm for building a
virtualized and intelligent RAN architecture. This paper presents a Machine
Learning (ML)-based Traffic Steering (TS) scheme to predict network congestion
and then proactively steer O-RAN traffic to avoid it and reduce the expected
queuing delay. To achieve this, we propose an optimized setup focusing on
safeguarding both latency and reliability to serve URLLC applications. The
proposed solution consists of a two-tiered ML strategy based on Naive Bayes
Classifier and deep Q-learning. Our solution is evaluated against traditional
reactive TS approaches that are offered as xApps in O-RAN and shows an average
of 15.81 percent decrease in queuing delay across all deployed SFCs.

该研究提出基于机器学习的交通引导方案以预测网络拥塞并在避免其的基础上通过优化设置来服务具备低延迟和高可靠性需求的 URLLC 应用。在 O-RAN 中，比传统反应式交通引导方法平均减少 15.81％的排队延迟。