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As we are moving forward to the 5G era, we are witnessing a transformation in the way networks are designed and behave, with the end-user placed at the epicenter of any decision. One of the most promising contributors towards this direction is the shift from Quality of Service (QoS) to Quality of Experience (QoE) service provisioning paradigms. QoE, i.e., the degree of delight or annoyance of a service as this is perceived by the end-user, paves the way for flexible service management and personalized quality monitoring. This is enabled by exploiting parametric QoE assessment models, namely specific formula-based QoE estimation methods. In this paper, recognizing a gap in the literature between the lack of a proper manual regarding the objective QoE estimation and the ever increasing interest from network stakeholders for QoE intelligence, we provide a comprehensive guide to standardized and state-of-the-art quality assessment models. More specifically, we identify and describe parametric QoE formulas for the most popular service types (i.e., VoIP, online video, video streaming, web browsing, Skype, IPTV and file download services), indicating the key performance indicators (KPIs) and major configuration parameters (MCPs) per type. Throughout the paper, it is revealed that KPIs and MCPs are highly variant per service type, and that, even for the same service, different factors contribute with a different weight on the perceived QoE. This finding can strongly enable a more meaningful resource provisioning across different applications compared to QoE-agnostic schemes. Overall, this paper is a stand-alone, self-contained repository of QoE assessment models for the most common applications, becoming a handy tutorial to parties interested in delving more into QoE network management topics.

The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. Moreover, a softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this paper, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.

No abstract

The existing mobile network infrastructure is currently optimized to support the Narrow-Band Internet of Things (NB-IoT), which is meant to provide numerous services in poor coverage areas, with ultra-low power consumption. The question that arises is whether (and to what extent) NB-IoT can meet the requirement for massive Machine Type Communications (mMTC) in the 5G era. In view of this, we study coverage enhancement in NB-IoT. We describe the subframe structure for various physical channels of NB-IoT and explain the repetition mechanism used for downlink transmissions in poor coverage areas. Focusing on optimizing this mechanism, we analyze how the latency and user blockage can be reduced by a proper selection of the downlink radio resources.