This paper presents a machine learning based handover management scheme for LTE to improve the Quality of Experience (QoE) of the user in the presence of obstacles. We show that, in this scenario, a state-of-the-art handover algorithm is unable to select the appropriate target cell for handover, since it always selects the target cell with the strongest signal without taking into account the perceived QoE of the user after the handover. In contrast, our scheme learns from past experience how the QoE of the user is affected when the handover was done to a certain eNB. Our performance evaluation shows that the proposed scheme substantially improves the number of completed downloads and the average download time compared to stateof-the-art. Furthermore, its performance is close to an optimal approach in the coverage region affected by an obstacle.

Spurred by a growing demand for higher-quality mobile services in vertical industries, 5G is integrating a rich set of technologies, traditionally alien to the telco ecosystem, such as machine learning or cloud computing. Despite the initial steps taken in prior research projects in Europe and beyond, additional innovations are needed to support vertical use cases. This is the objective of the 5Growth project: automate vertical support through (i) a portal connecting verticals to 5G platforms (a.k.a. vertical slicer), (ii) closed-loop machine-learning based Service Level Agreement (SLA) control, and (iii) end-to-end optimization. In this paper, we introduce a set of key 5Growth innovations supporting radio slicing, enhanced monitoring and analytics and integration of machine learning.

The increase of demand for mobile data services requires a massive network densification. A cost-effective solution to this problem is to reduce cell size by deploying a low-cost all-wireless Network of Small Cells (NoS). These hyperdense deployments create a wireless mesh backhaul amongst Small Cells (SCs) to transport control and data plane traffic. The semi-planned nature of SCs can often lead to dynamic wireless mesh backhaul topologies. This paper presents a self-organized backpressure routing scheme for dynamic SC deployments (BS) that combines queue backlog and geographic information to route traffic in dynamic NoS deployments. BS aims at relieving network congestion, whilst having a low routing stretch (i.e., the ratio of the hop count of the selected paths to that of the shortest path). Evaluation results show that, under uncongested conditions, BS shows similar performance to that of an Idealized Shortest PAth routing protocol (ISPA), while outperforming Greedy Perimeter Stateless Routing (GPSR), a state of the art geographic routing scheme. Under more severe traffic conditions, BS outperforms both GPSR and ISPA in terms of average latency by up to a 85% and 70%, respectively. We conducted ns-3 simulations in a wide range of sparse NoS deployments and workloads to support these performance claims.

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This article introduces the key innovations of the 5Growth service platform to empower verticals industries with an AI-driven automated 5G End-to-End (E2E) slicing solution which allows industries to achieve their service requirements. Specifically, we present multiple vertical pilots (Industry 4.0, Transportation and Energy), identify the key 5G requirements to enable them and analyze existing technical and functional gaps as compared to current solutions. Based on the identified gaps, we propose a set of innovations to address them with: (i) support of 3GPP-based RAN slices by introducing a RAN slicing model and providing automated RAN orchestration and control, (ii) an AI-driven closed-loop for automated service management with Service Level Agreement (SLA) assurance, and, (iii) Multi-domain solutions to expand service offerings by aggregating services and resources from different provider domains and also enable the integration of private 5G networks with public networks.

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In this paper, we present our approach to simulate mobility management scenarios for LTE heterogeneous network deployments defined by challenging radio propagation scenarios, such as the presence of coverage holes. We focus on the LTE module of ns-3, which is also known as LENA. Our contribution is twofold. On one hand, we propose a set of new features for the LTE module, including a new model for simulating obstacles blocking the propagation of radio signals, and a handover model suitable for the offline evaluation of mobility management solutions. On the other hand, we describe in detail the setup of the simulation scenario, highlighting the challenges that we faced during the implementation and discussing the chosen configuration parameters. Finally, we present some simulation results that we obtained with the proposed approach.

Dense small cell (SC) deployments pose new architectural and transport level requirements. It is expected that they will require local multi-hop wireless backhauls in which some SCs with high capacity links towards the core act as aggregation gateways. To enable scalable deployments, there is the need of routing schemes able to handle dynamicity coming not only from increasing traffic demands and varying wireless link quality, but also from incremental gateway deployment. This letter proposes Anycast Backpressure (AB), a practical distributed anycast routing protocol designed to scale with the number of gateways and to exploit path and gateway diversity. The distinguishing feature of AB is that under increasing traffic demands, it opportunistically exploits uncongested gateways. To distribute traffic among the gateways, AB solely relies on 1-hop neighborhood queue backlog and geographic information. Ns-3 simulations show that, compared to state-of-the-art single-path and multi-path multi-gateway schemes, AB results in up to 40% gains in aggregated throughput and 99% reduction in latency for the evaluated scenarios.