@CONFERENCE{Giuseppi2020,
author={Giuseppi, A. and De Santis, E. and Delli Priscoli, F. and Won, S.H. and Choi, T. and Pietrabissa, A.},
title={Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning},
journal={2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings},
year={2020},
doi={10.1109/WCNCW48565.2020.9124723},
art_number={9124723},
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. © 2020 IEEE.},
author_keywords={5G;  Markov Games;  Multi-Agent Reinforcement Learning;  Network Selection},
keywords={Heterogeneous networks;  Multi agent systems;  Queueing networks;  Reinforcement learning, Access points;  Control logic;  Control solutions;  Distributed control architectures;  Friend or Foe-Q learning;  Markov games;  Network selection;  Optimal networks, 5G mobile communication systems},
document_type={Conference Paper},
}

{"_id":"h7LcrpsEuK6DSnu7Z","bibbaseid":"giuseppi-desantis-dellipriscoli-won-choi-pietrabissa-networkselectionin5gnetworksbasedonmarkovgamesandfriendorfoereinforcementlearning-2020","author_short":["Giuseppi, A.","De Santis, E.","Delli Priscoli, F.","Won, S.","Choi, T.","Pietrabissa, A."],"bibdata":{"bibtype":"conference","type":"conference","author":[{"propositions":[],"lastnames":["Giuseppi"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["De","Santis"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Delli","Priscoli"],"firstnames":["F."],"suffixes":[]},{"propositions":[],"lastnames":["Won"],"firstnames":["S.H."],"suffixes":[]},{"propositions":[],"lastnames":["Choi"],"firstnames":["T."],"suffixes":[]},{"propositions":[],"lastnames":["Pietrabissa"],"firstnames":["A."],"suffixes":[]}],"title":"Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning","journal":"2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings","year":"2020","doi":"10.1109/WCNCW48565.2020.9124723","art_number":"9124723","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. © 2020 IEEE.","author_keywords":"5G; Markov Games; Multi-Agent Reinforcement Learning; Network Selection","keywords":"Heterogeneous networks; Multi agent systems; Queueing networks; Reinforcement learning, Access points; Control logic; Control solutions; Distributed control architectures; Friend or Foe-Q learning; Markov games; Network selection; Optimal networks, 5G mobile communication systems","document_type":"Conference Paper","bibtex":"@CONFERENCE{Giuseppi2020,\nauthor={Giuseppi, A. and De Santis, E. and Delli Priscoli, F. and Won, S.H. and Choi, T. and Pietrabissa, A.},\ntitle={Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning},\njournal={2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings},\nyear={2020},\ndoi={10.1109/WCNCW48565.2020.9124723},\nart_number={9124723},\nabstract={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. © 2020 IEEE.},\nauthor_keywords={5G; Markov Games; Multi-Agent Reinforcement Learning; Network Selection},\nkeywords={Heterogeneous networks; Multi agent systems; Queueing networks; Reinforcement learning, Access points; Control logic; Control solutions; Distributed control architectures; Friend or Foe-Q learning; Markov games; Network selection; Optimal networks, 5G mobile communication systems},\ndocument_type={Conference Paper},\n}\n\n","author_short":["Giuseppi, A.","De Santis, E.","Delli Priscoli, F.","Won, S.","Choi, T.","Pietrabissa, A."],"key":"Giuseppi2020","id":"Giuseppi2020","bibbaseid":"giuseppi-desantis-dellipriscoli-won-choi-pietrabissa-networkselectionin5gnetworksbasedonmarkovgamesandfriendorfoereinforcementlearning-2020","role":"author","urls":{},"keyword":["Heterogeneous networks; Multi agent systems; Queueing networks; Reinforcement learning","Access points; Control logic; Control solutions; Distributed control architectures; Friend or Foe-Q learning; Markov games; Network selection; Optimal networks","5G mobile communication systems"],"metadata":{"authorlinks":{}}},"bibtype":"conference","biburl":"https://raw.githubusercontent.com/AGiuseppi/AGiuseppi.github.io/master/pubs.bib","dataSources":["LnFruMKQos7repGrF","pNu9oaGWEHH7o22SP","5EzEYy34nHSRYHdGQ","gfA6sQjmaq9WsWFdK","iGZjjyKe2m2P6Gg4W","ZZdxNsgmnZu3pHpvP"],"keywords":["heterogeneous networks; multi agent systems; queueing networks; reinforcement learning","access points; control logic; control solutions; distributed control architectures; friend or foe-q learning; markov games; network selection; optimal networks","5g mobile communication systems"],"search_terms":["network","selection","networks","based","markov","games","friend","foe","reinforcement","learning","giuseppi","de santis","delli priscoli","won","choi","pietrabissa"],"title":"Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning","year":2020}