This paper proposes a deep-Q-network (DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process (MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT (radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing. In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.
2022, MACHINE INTELLIGENCE RESEARCH, Pages 127-137 (volume: 19)
Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection (01a Articolo in rivista)
De Santis Emanuele, Giuseppi Alessandro, Pietrabissa Antonio, Capponi Michael, Delli Priscoli Francesco
Gruppo di ricerca: Networked Systems