• Application of machine learning for energy efficiency in mobile networks 

      Sesto Castilla, David (Universitat Politècnica de Catalunya, 2017-09-14)
      Projecte Final de Màster Oficial
      Accés obert
      Realitzat a/amb:   Fundació i2cat / Cosmote
      Future generation networks (5G) will bring a new paradigm to network management, as the networks themselves will suffer evident changes that will imply new requirements in upper layers. The 5G-XHaul project, framed under ...
    • Intelligent management and control for Wi-Fi small cells 

      Sesto Castilla, David (Universitat Politècnica de Catalunya, 2016-09-09)
      Treball Final de Grau
      Accés obert
      In order to face the exponential growth of mobile data transmissions, it has been long since the concept of small cells is in the table, which provides high density deployments of small cells so as to provide a high capacity ...
    • SENSEFUL: An SDN-based joint access and backhaul coordination for Dense Wi-Fi Small Cells 

      García Villegas, Eduard; Sesto Castilla, David; Zehl, Sven; Zubow, Anatolij; Betzler, August; Camps Mur, Daniel (Institute of Electrical and Electronics Engineers (IEEE), 2017)
      Text en actes de congrés
      Accés obert
      Dense Small Cell networks are considered the most effective way to cope with the exponential increase in mobile traffic demand expected for the upcoming years and are one of the foundations of the future 5G. However, novel ...
    • Use of Machine Learning for energy efficiency in present and future mobile networks 

      Sesto Castilla, David; García Villegas, Eduard; Lyberopoulos, George; Theodoropoulou, Eleni (Institute of Electrical and Electronics Engineers (IEEE), 2019)
      Comunicació de congrés
      Accés obert
      Given the current evolution trends in mobile cellular networks, which is approaching us towards the future 5G paradigm, novel techniques for network management are in the agenda. Machine Learning techniques are useful for ...