Traffic steering and network selection in 5G networks based on Reinforcement Learning. In: European Control Conference 2020, 2020.
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.
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@inproceedings{Giuseppi2020f,
title = {Traffic steering and network selection in 5G networks based on Reinforcement Learning},
author = {Alessandro Giuseppi and Antonio Pietrabissa and Francesco Liberati and Roberto German\`{a} and Francesco Delli Priscoli},
doi = {10.23919/ECC51009.2020.9143837},
year = {2020},
date = {2020-07-20},
urldate = {2020-07-20},
booktitle = {European Control Conference 2020},
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.},
keywords = {5G, Network Selection, reinforcement learning, traffic steering},
pubstate = {published},
tppubtype = {inproceedings}
}