Towards Trustworthy AI for QoE prediction in B5G/6G Networks

José Luis Corcuera Bárcena, Pietro Ducange, Francesco Marcelloni, Giovanni Nardini, Alessandro Noferi, Alessandro Renda, Giovanni Stea, Antonio Virdis

Producción científica: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

4 Citas (Scopus)


The ability to forecast Quality of Experience (QoE) metrics will be crucial in several applications and services offered by the future B5G/6G networks. However, QoE timeseries forecasting has not been adequately investigated so far, mainly due to the lack of available realistic datasets. In this paper, we first present a novel QoE forecasting dataset obtained from realistic 5G network simulations and characterized by Quality of Service (QoS) and QoE metrics for a video-streaming application; then, we embrace the topical challenge of trustworthiness in the adoption of AI systems for tackling the QoE prediction task. We show how an eXplainable Artificial Intelligence (XAI) model, namely Decision Tree, can be effectively leveraged for addressing the forecasting problem. Finally, we identify federated learning as a suitable paradigm for privacy-preserving collaborative model training and outline the related challenges from both an algorithmic and 6G network support perspective.

Idioma originalInglés
PublicaciónCEUR Workshop Proceedings
EstadoPublicada - 2022
Publicado de forma externa
Evento1st International Workshop on Artificial Intelligence in Beyond 5G and 6G Wireless Networks, AI6G 2022 - Padova, Italia
Duración: 21 jul. 2022 → …


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