RT info:eu-repo/semantics/conferenceObject
T1 Efficient Optimization of Actor-Critic Learning for Constrained Resource Orchestration in RAN with Network Slicing
A1 Kanwal Janjua, Hafiza
A1 Miguel Jiménez, Ignacio de
A1 Durán Barroso, Ramón José
A1 González de Dios, Óscar
A1 Aguado Manzano, Juan Carlos
A1 Merayo Álvarez, Noemí
A1 Fernández Reguero, Patricia
A1 Lorenzo Toledo, Rubén Mateo
K1 Network Slicing
K1 Resource Orchestration
K1 Reinforcement Learning
K1 Constrained Optimization
AB 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.
PB Institute of Electrical and Electronics Engineers (IEEE)
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/62320
UL https://uvadoc.uva.es/handle/10324/62320
LA eng
NO 2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), Paris, France, 2023, pp. 100-104
DS UVaDOC
RD 12-nov-2024