Multilayered reinforcement learning approach for radio resource management
Document typeConference report
Rights accessRestricted access - publisher's policy
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.
CitationKevin, C. [et al.]. Multilayered reinforcement learning approach for radio resource management. A: International Conference on Computer Engineering and Network. "Multilayered Reinforcement Learning Approach for Radio Resource Management: proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013): July 20-21, 2013: Shanghai, China". Shangai: Springer, 2013, p. 1191-1199.
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