Towards an AI/ML-driven SMO Framework in O-RAN: Scenarios, Solutions, and Challenges
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917771401494528 |
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| author | Habibi, Mohammad Asif Han, Bin Saimler, Merve Pavon, Ignacio Labrador Schotten, Hans D. |
| author_facet | Habibi, Mohammad Asif Han, Bin Saimler, Merve Pavon, Ignacio Labrador Schotten, Hans D. |
| contents | The emergence of the open radio access network (O-RAN) architecture offers a paradigm shift in cellular network management and service orchestration, leveraging data-driven, intent-based, autonomous, and intelligent solutions. Within O-RAN, the service management and orchestration (SMO) framework plays a pivotal role in managing network functions (NFs), resource allocation, service provisioning, and others. However, the increasing complexity and scale of O-RANs demand autonomous and intelligent models for optimizing SMO operations. To achieve this goal, it is essential to integrate intelligence and automation into the operations of SMO. In this manuscript, we propose three scenarios for integrating machine learning (ML) algorithms into SMO. We then focus on exploring one of the scenarios in which the non-real-time RAN intelligence controller (Non-RT RIC) plays a major role in data collection, as well as model training, deployment, and refinement, by proposing a centralized ML architecture. Finally, we identify potential challenges associated with implementing a centralized ML solution within SMO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards an AI/ML-driven SMO Framework in O-RAN: Scenarios, Solutions, and Challenges Habibi, Mohammad Asif Han, Bin Saimler, Merve Pavon, Ignacio Labrador Schotten, Hans D. Networking and Internet Architecture The emergence of the open radio access network (O-RAN) architecture offers a paradigm shift in cellular network management and service orchestration, leveraging data-driven, intent-based, autonomous, and intelligent solutions. Within O-RAN, the service management and orchestration (SMO) framework plays a pivotal role in managing network functions (NFs), resource allocation, service provisioning, and others. However, the increasing complexity and scale of O-RANs demand autonomous and intelligent models for optimizing SMO operations. To achieve this goal, it is essential to integrate intelligence and automation into the operations of SMO. In this manuscript, we propose three scenarios for integrating machine learning (ML) algorithms into SMO. We then focus on exploring one of the scenarios in which the non-real-time RAN intelligence controller (Non-RT RIC) plays a major role in data collection, as well as model training, deployment, and refinement, by proposing a centralized ML architecture. Finally, we identify potential challenges associated with implementing a centralized ML solution within SMO. |
| title | Towards an AI/ML-driven SMO Framework in O-RAN: Scenarios, Solutions, and Challenges |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2409.05092 |