Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases
Driven by the emerging trend for transparent, open and programmable communications, Open Radio Access Network (O-RAN) constitutes the dominant architectural approach for deploying the future wireless networks. Towards standardizing and specifying the building blocks and principles of O-RAN, a coordi...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9754560/ |
| _version_ | 1818018217888579584 |
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| author | Anastasios Giannopoulos Sotirios Spantideas Nikolaos Kapsalis Panagiotis Gkonis Lambros Sarakis Christos Capsalis Massimo Vecchio Panagiotis Trakadas |
| author_facet | Anastasios Giannopoulos Sotirios Spantideas Nikolaos Kapsalis Panagiotis Gkonis Lambros Sarakis Christos Capsalis Massimo Vecchio Panagiotis Trakadas |
| author_sort | Anastasios Giannopoulos |
| collection | DOAJ |
| description | Driven by the emerging trend for transparent, open and programmable communications, Open Radio Access Network (O-RAN) constitutes the dominant architectural approach for deploying the future wireless networks. Towards standardizing and specifying the building blocks and principles of O-RAN, a coordinated global effort has been observed, mainly comprised of the O-RAN Alliance, the operators and several research activities. This paper presents the architectural aspects and the current status of O-RAN deployments, integrating both existing and ongoing activities from the O-RAN enablers. Furthermore, since the Artificial Intelligence and Machine Learning (AI/ML) act as key pillars for realizing O-RANs, a comprehensive view on the AI/ML functionality is provided as well. Additionally, a Network Telemetry (NT) architecture is also proposed to ensure end-to-end data collection and real-time analytics. To concretely illustrate the O-RAN supporting mechanisms for hosting AI/ML, we implemented two realistic ML algorithms: (i) a Supervised Learning (SL) based algorithm for cell traffic prediction using the training data of an open dataset and (ii) a Deep Reinforcement Learning (DRL) based algorithm for energy-efficiency maximization using a 5G-compliant simulator to obtain RAN measurements. We schematically demonstrate the AI/ML workflow for both ML-assisted algorithms through the usage of xApps running on the Radio Intelligent Controller (RIC), as well as we outline the role of the O-RAN components involved in the AI/ML loop. Combining the high-level architectural descriptions with a detailed presentation of ML-empowered resource allocation schemes, the paper discusses and summarizes the O-RAN disaggregation principles and the role of AI/ML embedded in future O-RAN deployments. |
| first_indexed | 2024-04-14T07:37:07Z |
| format | Article |
| id | doaj.art-bf76dcd94289427485dd427a8a9aaa81 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T07:37:07Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bf76dcd94289427485dd427a8a9aaa812022-12-22T02:05:40ZengIEEEIEEE Access2169-35362022-01-0110395803959510.1109/ACCESS.2022.31661609754560Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use CasesAnastasios Giannopoulos0https://orcid.org/0000-0002-8602-7401Sotirios Spantideas1Nikolaos Kapsalis2Panagiotis Gkonis3https://orcid.org/0000-0001-8846-1044Lambros Sarakis4https://orcid.org/0000-0002-3890-5476Christos Capsalis5Massimo Vecchio6https://orcid.org/0000-0003-4426-8220Panagiotis Trakadas7https://orcid.org/0000-0002-5146-5954Department of Ports Management and Shipping, National and Kapodistrian University of Athens, Euboea, GreeceDepartment of Ports Management and Shipping, National and Kapodistrian University of Athens, Euboea, GreeceDepartment of Ports Management and Shipping, National and Kapodistrian University of Athens, Euboea, GreeceDepartment of Digital Industry Technologies, National and Kapodistrian University of Athens, Euboea, GreeceDepartment of Digital Industry Technologies, National and Kapodistrian University of Athens, Euboea, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, Athens, GreeceFondazione Bruno Kessler, Trento, ItalyDepartment of Ports Management and Shipping, National and Kapodistrian University of Athens, Euboea, GreeceDriven by the emerging trend for transparent, open and programmable communications, Open Radio Access Network (O-RAN) constitutes the dominant architectural approach for deploying the future wireless networks. Towards standardizing and specifying the building blocks and principles of O-RAN, a coordinated global effort has been observed, mainly comprised of the O-RAN Alliance, the operators and several research activities. This paper presents the architectural aspects and the current status of O-RAN deployments, integrating both existing and ongoing activities from the O-RAN enablers. Furthermore, since the Artificial Intelligence and Machine Learning (AI/ML) act as key pillars for realizing O-RANs, a comprehensive view on the AI/ML functionality is provided as well. Additionally, a Network Telemetry (NT) architecture is also proposed to ensure end-to-end data collection and real-time analytics. To concretely illustrate the O-RAN supporting mechanisms for hosting AI/ML, we implemented two realistic ML algorithms: (i) a Supervised Learning (SL) based algorithm for cell traffic prediction using the training data of an open dataset and (ii) a Deep Reinforcement Learning (DRL) based algorithm for energy-efficiency maximization using a 5G-compliant simulator to obtain RAN measurements. We schematically demonstrate the AI/ML workflow for both ML-assisted algorithms through the usage of xApps running on the Radio Intelligent Controller (RIC), as well as we outline the role of the O-RAN components involved in the AI/ML loop. Combining the high-level architectural descriptions with a detailed presentation of ML-empowered resource allocation schemes, the paper discusses and summarizes the O-RAN disaggregation principles and the role of AI/ML embedded in future O-RAN deployments.https://ieeexplore.ieee.org/document/9754560/5GB5GO-RANAI/MLradio intelligent controllerresource allocation |
| spellingShingle | Anastasios Giannopoulos Sotirios Spantideas Nikolaos Kapsalis Panagiotis Gkonis Lambros Sarakis Christos Capsalis Massimo Vecchio Panagiotis Trakadas Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases IEEE Access 5G B5G O-RAN AI/ML radio intelligent controller resource allocation |
| title | Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases |
| title_full | Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases |
| title_fullStr | Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases |
| title_full_unstemmed | Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases |
| title_short | Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases |
| title_sort | supporting intelligence in disaggregated open radio access networks architectural principles ai ml workflow and use cases |
| topic | 5G B5G O-RAN AI/ML radio intelligent controller resource allocation |
| url | https://ieeexplore.ieee.org/document/9754560/ |
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