Traffic Steering is a crucial technology for wireless networks, and multiple efforts have been put into developing efficient Machine Learning (ML)-enabled traffic steering schemes for Open Radio Access Networks (O-RAN). Given the swift emergence of novel ML techniques, conducting a timely survey that comprehensively examines the ML-based traffic steering schemes in O-RAN is critical. In this article, we provide such a survey along with a case study of hierarchical learning-enabled traffic steering in O-RAN. In particular, we first introduce the background of traffic steering in O-RAN and overview relevant state-of-the-art ML techniques and their applications. Then, we analyze the compatibility of the hierarchical learning framework in O-RAN and further propose a Hierarchical Deep-Q-Learning (h-DQN) framework for traffic steering. Compared to existing works, which focus on single-layer architecture with standalone agents, h-DQN decomposes the traffic steering problem into a bi-level architecture with hierarchical intelligence. The meta-controller makes long-term and high-level policies, while the controller executes instant traffic steering actions under high-level policies. Finally, the case study shows that the hierarchical learning approach can provide significant performance improvements over the baseline algorithms.

翻译：流量引导是无线网络中的一项关键技术，针对开放无线接入网络（O-RAN）开发高效的机器学习（ML）赋能流量引导方案已有多项研究。鉴于新型机器学习技术的迅速涌现，及时开展一项全面审视O-RAN中基于机器学习的流量引导方案的综述至关重要。本文提供了这样一篇综述，并附带了O-RAN中分层学习赋能流量引导的案例研究。具体而言，我们首先介绍了O-RAN中流量引导的背景，并概述了相关的先进机器学习技术及其应用。接着，我们分析了分层学习框架在O-RAN中的适用性，并进一步提出了一种用于流量引导的分层深度Q学习（h-DQN）框架。与现有专注于单层架构及独立智能体的工作相比，h-DQN将流量引导问题分解为具有分层智能的双层架构。元控制器制定长期的高层策略，而控制器则根据高层策略执行即时流量引导动作。最后，案例研究表明，与基线算法相比，分层学习方法能带来显著的性能提升。