Multi-task Deep Learning based Environment and Mobility Detection for User Behavior Modeling

Marie-Line Alberi-Morel (1) , Illyyne Saffar (1) , Kamal Singh (2, 3) , César Viho (4)

1 Nokia Bell Labs [Nozay]
2 LHC - Laboratoire Hubert Curien
3 UJM - Université Jean Monnet - Saint-Étienne
4 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS

Cognition of user behavior can be seen as an efficient tool for automation of future mobile networks. As a matter of fact, it amplifies the intelligence of autonomic networks in a sense that the network is more aware of the operational context. However, predicting the context of mobile users is a prerequisite for inferring the user behavior. This paper deals with the user behaviour modeling. The model includes the prediction of two main features related to mobile user context: the environment and the mobility. Practically, the question is how and where the mobile user consumes the mobile services. We investigate Deep Learning based methods for simultaneously detecting the environment and the mobility state. We empirically evaluate the effectiveness of the proposed methods using real-time radio data, which has been massively gathered from multiple diversified situations of mobile users.

Mots clés: Index Terms-User Behavior; Preferences; Context of use; Deep Learning; Multi-task learning; Environment; Mobility

Domaines: Réseaux et télécommunications [cs.NI]

Marie-Line Alberi-Morel, Illyyne Saffar, Kamal Singh, César Viho. Multi-task Deep Learning based Environment and Mobility Detection for User Behavior Modeling. WMLC 2019 - International Workshop on Machine Learning for Communications, Jun 2019, Avignon, France. pp.1-7. ⟨hal-02113163⟩