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Abstract. To build a flexible and an adaptable architecture network supporting variety of services and their respective requirements, 5G NORMA introduced a network of functions based architecture breaking the major design principles followed in the current network of entities based architecture. This revolution exploits the advantages of the new technologies like Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in conjunction with the network slicing and multi-tenancy concepts. In this paper we focus on the concept of Software Defined for Mobile Network Control (SDM-C) network: its definition, its role in controlling the intra network slices resources, its specificity to be QoE aware thanks to the QoE/QoS monitoring and modeling component and its complementarity with the orchestration component called SDM-O. To operate multiple network slices on the same infrastructure efficiently through controlling resources and network functions sharing among instantiated network slices, a common entity named SDM-X is introduced. The proposed design brings a set of new capabilities to make the network energy efficient, a feature that is discussed through some use cases.

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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.