2020
Giuseppi, Alessandro; Pietrabissa, Antonio; Liberati, Francesco; Germanà, Roberto; Priscoli, Francesco Delli
Traffic steering and network selection in 5G networks based on Reinforcement Learning Proceedings Article
In: European Control Conference 2020, 2020.
Abstract | Links | BibTeX | Tags: 5G, Network Selection, reinforcement learning, traffic steering
@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}
}
Giuseppi, Alessandro; Santis, Emanuele De; Priscoli, Francesco Delli; Won, Seok Ho; Choi, Taesang; Pietrabissa, Antonio
Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning Proceedings Article
In: 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 1-5, 2020.
Abstract | Links | BibTeX | Tags: 5G, Markov Games, Multi-Agent Reinforcement Learning, Network Selection
@inproceedings{Giuseppi2020b,
title = {Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning},
author = {Alessandro Giuseppi and Emanuele De Santis and Francesco Delli Priscoli and Seok Ho Won and Taesang Choi and Antonio Pietrabissa},
doi = {10.1109/WCNCW48565.2020.9124723},
year = {2020},
date = {2020-04-01},
urldate = {2020-04-01},
booktitle = {2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)},
pages = {1-5},
abstract = {This paper presents a control solution for the optimal network selection problem in 5G heterogeneous networks. The control logic proposed is based on multi-agent Friend-or-Foe Q-Learning, allowing the design of a distributed control architecture that sees the various access points compete for the allocation of the connection requests. Numerical simulations validate conceptually the approach, developed in the scope of the EU-Korea project 5G-ALLSTAR},
keywords = {5G, Markov Games, Multi-Agent Reinforcement Learning, Network Selection},
pubstate = {published},
tppubtype = {inproceedings}
}