Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning


Alessandro Giuseppi, Emanuele De Santis, Francesco Delli Priscoli, Seok Ho Won, Taesang Choi, Antonio Pietrabissa: Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning. In: 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 1-5, 2020.

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

BibTeX (Download)

@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},
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}
}