Efficient optimization of actor-critic learning for constrained resource orchestration in RAN with network slicing

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.