A data-driven user steering algorithm for optimizing user experience in multi-tier LTE networks

Multi-tier cellular networks are a cost-effective solution for capacity enhancement in urban scenarios. In these networks, effective handover schemes are required to assign users to the most adequate layer. In this paper, a data-driven self-tuning algorithm for user steering is proposed to improve the overall Quality of Experience (QoE) in multi-carrier Long Term Evolution (TE) networks. Unlike classical approaches, user steering is achieved by changing Reference Signal Received Quality (RSRQ) based inter-frequency handover margins. To drive the tuning process, a novel indicator showing throughput changes in the vicinity of handovers is derived from connection traces. Method assessment is carried out in a dynamic system-level LTE simulator implementing a real multi-carrier scenario. Results show that the proposed algorithm significantly improves QoE figures obtained with a classical inter-frequency handover scheme based on Reference Signal Received Power (RSRP) measurements.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Modelling Slice Performance in Radio Access Networks through Supervised Learning

In 5G systems, the Network Slicing (NS) feature allows to deploy several logical networks customized for specific verticals over a common physical infrastructure. To make the most of this feature, cellular operators need models reflecting performance at slice level for re-dimensioning the Radio Access Network (RAN). Throughput is often regarded as a key perfor- mance metric due its strong impact on users demanding enhanced mobility broadband services. In this work, we present the first comprehensive analysis tackling slice throughput estimation in the down link of RAN-sliced networks through Supervised Learning (SL), based on information collected in the operations support system. The considered SL algorithms include support vector regression, k-nearest neighbors, ensemble methods based on decision trees and neural networks. All these algorithms are tested in two NS scenarios with single-service and multi-service slices. To this end, synthetic datasets with performance indicators and connection traces are generated with a system-level simulator emulating the activity of a live cellular network. Results show that the best model (i.e., combination of SL algorithm and input features) to estimate slice throughput may vary depending on the NS scenario. In all cases, the best models have shown adequate accuracy(i.e., error below 10%).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Un nuevo criterio basado en calidad de experiencia para el balance de carga en redes LTE

The increase in traffic and services in mobile networks has made network management a very complex task. This fact has motivated the development of many algorithms in a Self-Organized Network (SON) framework, such as Mobility Load Balancing (MLB). MLB achieves to solve congestion problems by sharing traffic demand among neighbour cells through the modification of handover parameters. However, it presents some limitations in current LTE networks. These limitations have a negative impact on end-user throughput and thus in Quality of Experience (QoE) perceived by end-users. In this paper, a sensitivity analysis of throughput according to Handover (HO) margins is presented and an alternative indicator for tuning HO margins is introduced, focusing on end-user throughput. The assessment is carried out in a trial LTE network. Results show that the proposed indicator improves network performance in terms of end-user throughput from that obtained with classical MLB algorithms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Limitaciones del equilibrio de carga para la mejora de la calidad de experiencia en redes LTE

Due to the huge increase in traffic and services in mobile networks, network management has changed its main focus from Quality of Service (QoS) to Quality of Experience (QoE). In addition, SON (Self Organizing Networks) techniques have been developed to automate network management, being load balancing a key use case. Load balancing aims to balance the traffic among adjacent cells in the hope that this balance will decrease the overall blocking ratio, thus increasing the total carried traffic in the network. Nevertheless, this technique may fail when QoE perspective is considered. In this work, a QoE network sensitivity analysis is performed in a LTE network with different services and traffic conditions. Different traffic sharing techniques are tested and limitations of classical cell load balancing algorithm are shown when a QoE performance perspective is considered.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A QoE-driven traffic steering algorithm for LTE Networks

Due to the huge increase in traffic and services in mobile networks, network management has changed its main focus from Quality of Service (QoS) to a Quality of Experience (QoE) perspective. In addition, SON (Self Organizing Networks) techniques have been developed to automate network management, being load balancing a key use case. Load balancing aim is to balance the traffic among adjacent cells. This balance is expected to decrease the overall blocking ratio, thus increasing the total carried traffic in the network. Nevertheless, these techniques may fail when QoE perspective is considered. In this work, a novel QoE balancing algorithm is proposed to reach QoE equilibrium in a realistic LTE network with different services. The proposed balancing approach is tested and compared with classical techniques by means of simulations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A data-driven scheduler model for QoE assessment in a LTE radio network planning tool

The use of static system-level simulators is common practice for estimating the impact of re-planning actions in cellular networks. In this paper, a modification of a classical static Long Term Evolution (LTE) simulator is proposed to estimate the Quality of Experience (QoE) provided in each location on a per-service basis. The core of the simulator is the estimation of radio connection throughput on a location and service basis. For this purpose, a new analytical performance model for the packet scheduling process in a multi-service scenario is developed. Model parameters can easily be adjusted with information from radio connection traces available in the network management system. The simulation tool is validated with a large trace dataset taken from a live LTE network

Asignación de unidades de banda base en redes de acceso radio centralizadas por teoría de grafos

En este trabajo, se presentan varios métodos para planificar la asignación de celdas a unidades de procesado en banda base en una red de acceso radio centralizada que incluye macroceldas y celdas pequeñas de interior. El proceso de asignación se formula como un problema de partición de grafos, que se resuelve mediante algoritmos heurísticos. La validación se realiza con una herramienta de planificación radio que comprueba los niveles de interferencia en un escenario de red LTE heterogénea. Los resultados demuestran que el método multiarranque adaptativo por agrupamiento es el que obtiene mejores resultados.Ministerio de Ciencia, Innovación y Universidades (RTI2018-099148-B-I00). Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Statistical model for mobile user positioning based on social information.

In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems to pinpoint problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the sceario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from their traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged messages from two social networks (Twitter and Flickr).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A quality of experience evaluation method for an UAV first person view system

Unmanned Aerial Vehicles (UAV) communication systems are an increasingly widespread and emerging technology due to their flexibility, low cost and usability properties. Hence, the demand for Beyond Visual Line of Sight (BVLOS) cases that require large data transmission and low latency in cellular networks are increasingly. In this work, the assembly, integration and networking of a UAV quadrotor for First Person View (FPV) system connected by LTE is presented. Different configurations of the link between the UAV and the Ground Control Station (GCS) are proposed, such as connection by LTE cloud-based server, direct LTE connection and direct WiFi connection. With these configurations, experiments are carried out to characterise the network metrics that model this service according to the telemetry, control and video traffic. The main contribution is the definition of a closed mathematical expression provided to define the Quality of Experience (QoE) for FPV use cases considering the video quality in terms of Video Multimethod Assessment Fusion (VMAF), network latency and video resolution as inputs. This expression will be applied to lab experiments taking into account link performance, in which network changes based in packet loss and latency alterations will be introduced to measure the QoE of the UAV system.Campus de Excelencia Internacional Andalucía Tech

Mejora de la calidad de experiencia en redes LTE multi-portadora

In multi-tier cellular networks, effective handover schemes are required to assign users to the most adequate layer. In this paper, a data-driven strategy for traffic steering is proposed to improve the overall Quality of Experience (QoE) in multi-carrier Long Term Evolution (LTE) networks. Unlike classical approaches, traffic steering is tackled by tuning Reference Signal Received Quality (RSRQ)-based inter-frequency handover margins. The tuning process is driven by a novel indicator derived from connection traces showing QoE changes in the vicinity of handovers. Method assessment is carried out in a dynamic system-level simulator implementing a real multicarrier LTE scenario. Results show that the proposed algorithm significantly improves QoE figures from the initial operator solution.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech