Blar i NTNU Open på forfatter "Trivisonno, Riccardo"

Viser treff 1-4 av 4

    • Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks 

      Schwarzmann, Susanna; Marquezan, Clarissa Cassales; Trivisonno, Riccardo; Nakajima, Shinichi; Zinner, Thomas (Chapter, 2020)
      Since their first release, 5G systems have been enhanced with Network Data Analytics Functionalities (NWDAF) as well as with the ability to interact with 3rd parties' Application Functions (AFs). Such capabilities enable ...

    • Estimating Video Streaming QoE in the 5G Architecture Using Machine Learning 

      Schwarzmann, Susanna; Marquezan, Clarissa; Bosk, Marcin; Liu, Huiran; Trivisonno, Riccardo; Zinner, Thomas Erich (Chapter, 2019)
      Compared to earlier mobile network generations, the 5G system architecture has been significantly enhanced by the introduction of network analytics functionalities and ex- tended capabilities of interacting with third party ...

    • ML-based QoE Estimation in 5G Networks Using Different Regression Techniques 

      Schwarzmann, Susanna; Marquezan, Clarissa Cassales; Trivisonno, Riccardo; Nakajima, Shinichi; Barriac, Vincent; Zinner, Thomas (Journal article; Peer reviewed, 2022)
      Monitoring and providing customers with a satisfying Quality of Experience (QoE) is a crucial business incentive for mobile network operators (MNOs). While the MNO is capable of monitoring a vast amount of network-related ...

    • Using 5G QoS Mechanisms to Achieve QoE-Aware Resource Allocation 

      Bosk, Marcin; Gajic, Marija; Schwarzmann, Susanna; Lange, Stanislav; Trivisonno, Riccardo; Marquezan, Clarissa; Zinner, Thomas Erich (Chapter, 2021)
      Network operators generally aim at providing a good level of satisfaction to their customers. Diverse application demands require the usage of beyond best-effort resource allocation mechanisms, particularly in resource-constrained ...