The evolution towards 5G mobile networks will be characterized by an increasing number of wireless devices, an increasing device and service complexity, and the requirement to access mobile services ubiquitously. Two key enablers will allow for realizing the vision of 5G: very dense deployments and centralized processing. This article discusses the challenges and requirements on the design of 5G mobile networks based upon these two key enablers. It discusses how cloud technologies and a flexible functionality assignment in radio access networks enable network densification and centralized operation of the radio access network over heterogeneous backhaul networks. The article describes the fundamental concepts, shows how to evolve the 3GPP LTE architecture, and outlines the expected benefits.

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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.

This paper presents the approach of extending cellular networks with millimeter-wave backhaul and access links. Introducing a logical split between control and user plane will permit full coverage while seamlessly achieving very high data rates in the vicinity of mm-wave small cells

We characterize a two tier heterogeneous network, consisting of classical sub-6GHz macro cells, and multi Radio Access Technology (RAT) small cells able to operate in sub-6GHz and millimeter-wave (mm-wave) bands.For optimizing coverage and to balance loads, we propose a two-step mechanism based on two biases for tuning the tier and RAT selection, where the sub-6GHz band is used to speed-up the initial access procedure in the mmwave RAT. First, we investigate the effect of the biases in terms of signal to interference plus noise ratio (SINR) distribution, cell load, and user throughput. More specifically, we obtain the optimal biases that maximize either the SINR coverage or the user downlink throughput. Then, we characterize the cell load using the mean cell approach and derive upper bounds on the overloading probabilities. Finally, for a given traffic density, we provide the small cell density required to satisfy system constraints in terms of overloading and outage probabilities. Our analysis highlights the importance of deploying dual band small cells in particular when small cells are sparsely deployed or in case of heavy traffic.