In this paper we face the challenge of designing self-tuning systems governing the working parameters of base stations on a mobile network system to optimize the quality of service and the economic benefit of the operator. In order to accomplish this double objective, we propose the combined use of fuzzy logic and reinforcement learning to implement a self-tuning system using a novel approach based on a two-agent system. Different combinations of reinforcement learning techniques, on both agents, have been tested to deduce the optimal approach. The best results have been obtained applying the Q-learning technique on both agents, clearly outperforming the alternative of using non-learning algorithms. ; Peer Reviewed ; Postprint (published version)
Multilayered reinforcement learning approach for radio resource management
2013-01-01
Miscellaneous
Electronic Resource
English
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils , Àrees temàtiques de la UPC::Matemàtiques i estadística::Lògica matemàtica , Fuzzy logic , Mobile communication systems , Computer networks , Learning algorithms , Optimization , Quality of service , Lògica difusa , Comunicacions mòbils , Sistemes de
Multi-layered reinforcement learning approach for radio resource management
BASE | 2014
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