Conference paper Open Access

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

Giuseppi, Alessandro; De Santis, Emanuele; Delli Priscoli, Francesco; Won, Seok Ho; Choi, Taesang; Pietrabissa, Antonio

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.
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