Traffic Steering and Network Selection in 5G Networks based on Reinforcement Learning
Francesco Delli Priscoli, Alessandro Giuseppi, Francesco Liberati, Antonio Pietrabissa
Abstract
This paper presents a controller for the problem of Network Selection in 5G Networks, based on Reinforcement Learning. The problem of Network Selection and Traffic Steering is modeled as a Markov Decision Process and a Q- Learning based control solution is designed to meet 5G requirements, such as Quality of Experience (QoE) maximization, Quality of Service (QoS) assurance and load balancing. Numerical simulations preliminarily validate the proposed approach on a simulated scenario considered in the European project H2020 5G-ALLSTAR.
Topics & Concepts
Reinforcement learningComputer scienceMarkov decision processQuality of serviceQuality of experienceSelection (genetic algorithm)Markov processMaximizationControl (management)Process (computing)Computer networkDistributed computingArtificial intelligenceMathematical optimizationMathematicsOperating systemStatisticsSoftware-Defined Networks and 5GAdvanced Optical Network TechnologiesAdvanced Photonic Communication Systems