Online learning for self-optimization in heterogeneous networks
[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. [ENG] This doctoral dissertation has been presented in the form of thesis by publication. These problems have been addressed by means of interference coordination (IC) and energy saving (ES) mechanisms. Although the configuration of these two mechanisms has been addressed separately so far, we show in this thesis that they are highly coupled. Moreover, the configuration of IC and ES is commonly addressed using network models, which presents several limitations. In this thesis, we consider the self-optimization functionality within the Self-Organizing Networks (SON) paradigm, which is intended to address these problems by allowing the network to autonomously configure its parameters while it is operating. To implement the self-optimization functionality, we propose the use of online learning algorithms, which learn efficient network configurations from experience without explicitly knowing the accurate mathematical model of the network beforehand. The first part of this thesis addresses the configuration of the IC mechanism in HetNets. We propose several online learning model-free solutions based on different techniques such as Response Surface Method (RSM) and Multi-Armed Bandit (MAB) algorithms. We also consider stochastic constraints in the learning process. In the second part, we address the joint problem of IC and ES in HetNets proposing several solutions based on Dynamic Programming, Contextual Multi-Armed Bandit algorithms, and Machine Learning tools such as Neural Networks and Gaussian processes.
Optimización matemática, Aprendizaje automático, Redes de ordenadores
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