Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach
<|reference_start|>Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach: 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.<|reference_end|>
@article{habib2024machine,
    title={Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on
  Hierarchical Learning Approach},
    author={Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera, Yigit Ozcan,
  Medhat Elsayed, Majid Bavand, Raimundas Gaigalas, Melike Erol-Kantarci},
    journal={arXiv preprint arXiv:2409.20391},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20391},
    primaryClass={cs.NI}
}