Intelligent QoS-aware slice resource allocation with user association parameterization for beyond 5G O-RAN-based architecture using DRL
The escalating demands of next-generation, beyond5G services necessitate innovative approaches to dynamic resource management, critical for satisfying end-user expectations and maintaining quality of service (QoS). This study leverages the Open Radio Access Network (O-RAN) architecture’s flexibility...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| 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:2117/417773 |
| Acceso en línea: | https://hdl.handle.net/2117/417773 https://dx.doi.org/10.1109/TVT.2024.3483288 |
| Access Level: | acceso abierto |
| Palabra clave: | Slicing DRL QoS Resource allocation and management URLLC eMBB KPI Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The escalating demands of next-generation, beyond5G services necessitate innovative approaches to dynamic resource management, critical for satisfying end-user expectations and maintaining quality of service (QoS). This study leverages the Open Radio Access Network (O-RAN) architecture’s flexibility and programmability to introduce a novel deep reinforcement learning (DRL) strategy for QoS-aware intraslice resource allocation. By employing intelligent agents and a Deep Q-Network (DQN)-based framework, our approach precisely tailors resource distribution within O-RAN, optimizing for enhanced mobile broadband (eMBB) and ultra-reliable lowlatency communications (URLLC) slices. The proposed method, featuring intelligent QoS-aware resource allocation (IQRA) and its low-complexity variant (LIQRA), demonstrates significant throughput improvements for eMBB by 11.5% compared to state-of-the-art (SOTA) methods and reduces URLLC latency by 19.94% and 16.54%, achieving up to 45.5% lower latency than baseline. A streamlined algorithm effectively reduces computational complexity, ensuring robust performance under resource constraints. Simulation results underscore the algorithm’s ability to substantially enhance 5G network slice performance, offering a parameterized solution for user association in O-RAN networks using DRL. This research not only meets high key performance indicators (KPIs) but also advances edge intelligence, fostering a more responsive network ecosystem. |
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