Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929521898291200 |
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| author | Habib, Md Arafat Zhou, Hao Iturria-Rivera, Pedro Enrique Ozcan, Yigit Elsayed, Medhat Bavand, Majid Gaigalas, Raimundas Erol-Kantarci, Melike |
| author_facet | Habib, Md Arafat Zhou, Hao Iturria-Rivera, Pedro Enrique Ozcan, Yigit Elsayed, Medhat Bavand, Majid Gaigalas, Raimundas Erol-Kantarci, Melike |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach Habib, Md Arafat Zhou, Hao Iturria-Rivera, Pedro Enrique Ozcan, Yigit Elsayed, Medhat Bavand, Majid Gaigalas, Raimundas Erol-Kantarci, Melike Networking and Internet Architecture 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. |
| title | Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2409.20391 |