Online learning for self-optimization in heterogeneous networks
Director/a
Alcaraz Espín, Juan JoséEscuela/Centro
Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad
Universidad Politécnica de CartagenaPrograma de doctorado
Programa de Doctorado en Tecnologías de la Información y las Comunicaciones por la Universidad Politécnica de CartagenaFecha de lectura
2019-10-07Fecha de publicación
2019Editorial
José Antonio Ayala RomeroPalabras clave
Optimización matemáticaAprendizaje automático
Redes de ordenadores
Resumen
[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 ...
Colecciones
- Tesis [536]
El ítem tiene asociados los siguientes ficheros de licencia:
Redes sociales