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Multi-layered reinforcement learning approach for radio resource management

Autor
Kevin, C.; Gorricho, J.; Serrat, J.; Hu, Z.
Tipus d'activitat
Article en revista
Revista
Lecture notes in electrical engineering
Data de publicació
2014-02-07
Volum
277
Pàgina inicial
1191
Pàgina final
1199
DOI
https://doi.org/10.1007/978-3-319-01766-2_135 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/21540 Obrir en finestra nova
URL
http://link.springer.com/chapter/10.1007/978-3-319-01766-2_135 Obrir en finestra nova
Resum
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...
Citació
Kevin, C. [et al.]. Multi-layered reinforcement learning approach for radio resource management. "Lecture Notes in Electrical Engineering", 07 Febrer 2014, vol. 277, p. 1191-1199.
Paraules clau
Cellular networks, Fuzzy logic, Radio resource management, Reinforcement learning
Grup de recerca
MAPS - Management, Pricing and Services in Next Generation Networks

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