QoS-Driven Scheduling in 5G Radio Access Networks - A Reinforcement Learning Approach

Abstract

The expected diversity of services and the variety of use cases in 5G networks will require a flexible Radio Resource Management able to satisfy the heterogeneous Quality of Service (QoS) requirements. Classical scheduling strategies have been designed to deal mainly with some particular QoS requirements for specific traffic types. To improve the scheduling performance, this paper proposes an innovative scheduler framework, that selects at each transmission time interval, the appropriate scheduling strategy capable to maximize the users$'$ satisfaction measure in terms of distinct QoS requirements. Neural networks and the Reinforcement Learning paradigm are jointly used to learn the best scheduling decision based on the past experiences. The simulation results show very good convergence properties for the proposed policies, and notable QoS improvements with the respect to the baseline scheduling solutions.