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Título
Efficient Optimization of Actor-Critic Learning for Constrained Resource Orchestration in RAN with Network Slicing
Autor
Congreso
2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)
Año del Documento
2023
Editorial
Institute of Electrical and Electronics Engineers (IEEE)
Descripción Física
4 p.
Documento Fuente
2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), Paris, France, 2023, pp. 100-104
Abstract
Network Slicing (NS) is a key enabler of the 5G network ecosystem due to its potential to provide distinct services over the same physical infrastructure. However, the necessity to optimally orchestrate resources for heterogeneous demands is crucial when dealing with resource constraints and Quality-of-Service (QoS) requirements. We consider a radio access network scenario providing NS over multiple base stations (BS) with limited resources, and we design an efficient resource orchestration technique, based on reinforcement learning, which optimizes resource utilization among different services while satisfying the constraints and complying with Service Level Agreement (SLA) and QoS requirements. The proposed technique makes use of the Trust Region Method to formulate the orchestration objective function and satisfy the constraints and is then optimized via Kronecker Factored Approximate Curvature (K-FAC). Extensive simulations demonstrate that the proposed technique outperforms other Reinforcement Learning (RL) algorithms, reaching 99% of QoS and SLA satisfaction while assuring bandwidth constraints.
Palabras Clave
Network Slicing
Resource Orchestration
Reinforcement Learning
Constrained Optimization
Patrocinador
EU H2020 MSCA ITN-ETN IoTalentum (grant no. 953442)
Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)
Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42 financiado por MCIN/AEI/10.13039/501100011033)
Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)
Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42 financiado por MCIN/AEI/10.13039/501100011033)
Version del Editor
Idioma
eng
Tipo de versión
info:eu-repo/semantics/acceptedVersion
Derechos
openAccess
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