• DocumentCode
    2246466
  • Title

    A reinforcement learning approach for QoS/QoE model identification

  • Author

    Canale, S. ; Delli Priscoli, F. ; Monaco, S. ; Palagi, L. ; Suraci, V.

  • Author_Institution
    Department of Computer, Control and Management Engineering “Antonio Ruberti” of the University of Rome “La Sapienza”, Rome, Italy
  • fYear
    2015
  • fDate
    28-30 July 2015
  • Firstpage
    2019
  • Lastpage
    2023
  • Abstract
    In the last decade, researchers has focused their studies on the mathematical relation between the Quality of Service (QoS) and the user Quality of Experience (QoE). This paper investigates the problem of modelling the user QoE feedback in the next generation networks. The problem has been formulated and solved using a reinforcement learning technique. The proposed approach is innovative since it does not require an explicit knowledge of the mathematical model describing the network dynamics or the QoS/QoE relationship since it is learnt on-line. Simulation results shows that the proposed solution can adapt dynamically to the user behavior.
  • Keywords
    Estimation; Internet; Irrigation; Learning (artificial intelligence); Mathematical model; Measurement; Quality of service;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2015 34th Chinese
  • Conference_Location
    Hangzhou, China
  • Type

    conf

  • DOI
    10.1109/ChiCC.2015.7259941
  • Filename
    7259941