A set of DRL-based xApps for joint RAN/MEC resource allocation and slicing management in O-RAN
The evolution of wireless communication technologies, moving from the established fifth-generation (5G) to beyond 5G and ultimately the sixth-generation (6G), highlights the need for a significant shift in architectural approach. This transformation is essential to effectively accommodate the signif...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/411732 |
| Acceso en línea: | https://hdl.handle.net/2117/411732 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning--Technological innovations O-RAN DRL 5G Resource Allocation Slicing MEC Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | The evolution of wireless communication technologies, moving from the established fifth-generation (5G) to beyond 5G and ultimately the sixth-generation (6G), highlights the need for a significant shift in architectural approach. This transformation is essential to effectively accommodate the significant increase in multi-connectivity and on-demand services that is expected in the near future. Machine Learning (ML) and more specifically Deep Reinforcement Learning (DRL) are a promising approach to solving the aforementioned challenges. In this project, an approach for dynamic resource allocation and management of both the Radio Acess Network (RAN) and Multi-Access Edge Computing (MEC) leveraging Deep Q-Network (DQN) is proposed. Two DQN models are implemented for admission control and maintenance of RAN-level slicing, in order to be deployed as Extended Applications (xApps) within the O-RAN architectural framework. This methodology ensures effective resource allocation while maintaining the Quality of Services (QoS). The proposed solution is validated through simulation results, demonstrating its effectiveness in improving network efficiency and performance in future 5G and 6G networks. Further stages include the implementation of Federated Learning to deploy the proposed models in multiple mobile scenarios and the correspondent emulation in real-scale frameworks. |
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