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.

翻译：下一代网络的目标是改进当前网络范式，特别是在提供更高数据速率、近实时延迟和近乎完美的服务质量方面。然而，现有的无线接入网络（RAN）架构缺乏足够的灵活性和智能性来满足这些需求。开放无线接入网络（O-RAN）是一种构建虚拟化且智能化的RAN架构的有前景的范式。本文提出一种基于机器学习的流量引导（TS）方案，用于预测网络拥塞，然后主动引导O-RAN流量以避免拥塞并减少预期的排队延迟。为实现这一目标，我们提出了一种优化设置，重点关注保障URLLC应用的延迟和可靠性。所提出的方案包含一种基于朴素贝叶斯分类器和深度Q学习的两层ML策略。我们将该解决方案与O-RAN中作为xApps提供的传统反应式TS方法进行对比评估，结果显示在所有部署的SFC中，排队延迟平均降低了15.81%。