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 · 2020
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