Despite QoE evaluation methodologies in mobile networks are well-known and a largely investigated topic for human communication services, such as voice and video applications, several challenges remain yet unresolved. Primary the lack of industry-agreed definitions of the End-to-End performance indicators represents a major issue. In that picture, 5G technologies are anticipated to introduce a wide variety of new use cases and diverse services, and with that further challenges to QoE assessment. Particularly, machine communication services comprise performance inter-dependencies across machines (i.e. systemic effects), which have yet to be fully understood and modelled. Thus, this paper seeks to present the foreseen evolution of the QoE evaluation and optimization methodologies in mobile networks suitable for 5G to accommodate those challenges. Building on today's best practises, we describe a flexible QoE service model framework that relies on Key Quality Indicators (KQIs) allowing to estimate the objective QoE of specific services, while reflecting the drivers for network optimization and accounting for the Mobile Network Operator's strategy and the actual network design. We validate the proposed framework in a live mobile network of a Tier-1 LTE operator, proving the capability to well define the performance degradation phases, therefore making service quality management more effective. Being technology agnostic, we conclude on the model applicability to 5G and outline few potential adjustments.

The demand on network infrastructures is changing. The increasing number of connected devices, along with growing demand, are creating an unsustainable future for the Internet. The recently introduced concept of Fog computing predicts a future Internet where general compute power is ubiquitous, extending the Cloud right the way to the network edge. In turn, this acts as a catalyst for Network Functions Virtualisation (NFV), increasing the potential infrastructure locations for deploying new services, specifically ones that can cater to the demands of the changing Internet. However, current realisations of NFV typically host network functions in homogeneous, centralised servers in Cloud infrastructures. This is in contrast to the Fog where environments are both distributed and heterogeneous, thus current management and orchestration platforms suffer from suboptimal service deployment. With the use of a multiple use cases, and a novel auctioning orchestration method, this paper presents Siren, which is an orchestrator for network functions in the Cloud to Fog continuum.

Edge computing refers to data storage and processing at the edge of a network instead of cloud data centers. In edge com-puting, user data can be preprocessed to reduce the network traffic and load of the cloud system. Module deployment scheme is vital in edge computing since it has a significant effect on the performance and the resource utilization of the edge computing system. Our objective is to maximize satisfied user requests under the constraints of limited communication band-width and storage capacity of devices where the modules are deployed. Since the optimization problem is NP-hard, we pro-pose a heuristic algorithm based on relaxation and rounding techniques to solve this problem. The simulation results show that our algorithm satisfies more user requests than previous work.

Vertical markets and industries are addressing a large diversity of heterogeneous services, use cases, and applications in 5G. It is currently common understanding that for networks to be able able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is required. Moreover, we are currently observing a softwarization and cloudification of the communication networks, where network functions (NFs) are translated from monolithic pieces of equipment to programs running over a shared pool of computational and communication resources. However, this novel architecture paradigm requires new solutions to exploit its inherent flexibility. In this paper, we introduce the concept of resource elasticity as a key means to make an efficient use of the computational resources in 5G systems. Besides establishing a definition as well as a set of requirements and key performance indicators (KPIs), we propose mechanisms for the exploitation of elasticity in three different dimensions, namely computational elasticity in the design and scaling of NFs, orchestration-driven elasticity by flexible placement of NFs, and slice-aware elasticity via cross-slice resource provisioning mechanisms. Finally, we provide a succinct analysis of the architectural components that need to be enhanced to incorporate elasticity principles.

Unmanned aerial vehicles (UAVs) are playing an important role in serving ground users as base stations (BSs) during temporary events, or after terrestrial BSs fail. UAV deployment is a fundamental and vital problem which directly affects the coverage capacity and user experience. Especially, while deploying a UAV backbone network, the cost and the quality of service (QoS) of the ground users as well as the connectivity among UAVs need to be considered jointly. In this paper, the deployment problem is modeled as minimizing the amount of deployed UAVs while guaranteeing the coverage capacity and connectivity among UAVs. Further, a novel UAV deployment scheme is proposed where a heuristic algorithm is adopted to delete locations from the initially defined candidate UAV locations iteratively, and UAVs are deployed at the remaining locations finally. The simulation results show that our UAV deployment scheme can achieve minimum deployed UAVs while ensuring QoS requirements and a robust network among UAVs.

There is a continuous paradigm change in network management and automation in 5G networks due the high dynamicity of the radio topology, density of the users and demand for 5G services. The Morphnet architecture aims to maintain a flexible reconfigurable E2E mobile network architecture required for supporting various 5G services with different requirements realized through the use of dynamic network slices. For this purpose, initially the radio topology is adapted to current environmental, network and demand parameters and secondly, the network slices are adapted to this new radio topology. By using the hierarchical and grid-based MANO management modules each focussing on a different domain/cloud and utilizing SDN and NFV technologies, the adaptation and local repair of slices are enabled. As a result, Morphnet provides a network management framework that can be operated either in a standalone fashion to provide end-to-end network adaptation to different parameters or as a fine-grained assisting mechanism to existing end-to-end solutions in dynamic function placement tasks.

The tactile internet requires an ultra short end-to-end (E2E) latency of about $1$~ms and is proposed as one of the fifth-generation (5G) wireless cellular network applications. In a typical 5G framework, inclusion of tactile data that impose different delay requirements than traditional data to the system, makes the system implementation more challenging. This paper is concerned with resource allocation to maximize the uplink sum rate of traditional data while satisfying the delay requirements for tactile data. To do so, we propose a practical model for delay in which the queuing delay both at the source and the base station is considered. We use sparse code multiple access (SCMA), one of the candidate multiple access techniques in 5G cellular network, in our system model. Consequently, for a given number of traditional data streams, we can quantify the reduction in the traditional data sum rate caused by increasing the number of tactile data streams. In addition, we investigate the effect of quality of service coefficient, referred to as $\theta$ on the uplink sum rate of traditional data and show that there is a threshold behavior such that if $\theta$ gets larger than a certain value, the sum rate of traditional data drastically decreases toward zero.

5G New Radio (NR) is moving towards flexibility and adaptability and is expected to provide optimized support for diverse 5G use case categories. With the introduction of flexible waveforms and numerologies in 5G, there is a need for new scheduling and resource allocation techniques. Using different non-orthogonal numerologies in the network complicates the resource allocation process since it introduces numerology multiplexing. In this paper, we propose a channel quality and Quality of Service (QoS) aware resource allocation scheme for multi-numerology 5G networks. We implement a frequency domain packet scheduler which allocates packets to Resource Blocks to achieve higher spectral efficiency, maintain fairness, and satisfying the QoS requirements of users with different non-orthogonal numerologies. The results show that our algorithm achieves high throughput in different traffic mixes with different numerology QoS requirements over varying number of users.

In mobile networks, capacity can be increased by employing small cells. This approach enlarges network infrastructures and increases the amount of energy consumption. Moreover, traffic demands in a mobile network are not fixed in time or space, and they cannot be accurately predicted in advance. Therefore, network functions such as base station scheduling, cell zooming or user-to-base-station association need to be dynamically controlled to conserve more energy. Most of the related work in the literature consider these challenges separately. In this paper, we present a joint base station scheduling, zooming and user association technique to reduce energy consumption and to enhance the quality of service. The major contribution of this paper is reducing power consumption by turning off redundant base stations and adapting transmit power of active base stations to network condition while maintaining the quality of service experienced by users. We validated the proposed technique by using MATLAB optimization toolbox. In this work, we reduced energy consumption by 47% in comparison to an alternative solution.