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 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

Traffic Steering in B5G Sliced Radio Access Networks.

In 5G and beyond wireless systems, Network Slicing (NS) feature will enable the coexistence of extremely different services by splitting the physical infrastructure into several logical slices tailored for a specific tenant or application. In sliced Radio Access Networks (RANs), an optimal traffic sharing among cells is key to guarantee Service Level Agreement (SLA) compliance while minimizing operation costs. The configuration of network functions leading to that optimal point may depend on the slice, claiming for slice-aware traffic steering strategies. This work presents the first data-driven algorithm for sliceaware traffic steering by tuning handover margins (a.k.a. mobility load balancing). The tuning process is driven by a novel indicator, derived from connection traces, showing the imbalance of SLA compliance among neighbor cells per slice. Performance assessment is carried out with a system-level simulator implementing a realistic sliced RAN offering services with different throughput, latency and reliability requirements. Results show that the proposed algorithm improves the overall SLA compliance by 9% in only 15 minutes of network activity compared to the case of not steering traffic, outperforming two legacy mobility load balancing approaches not driven by SLA

Modelado de rendimiento de segmento en redes de acceso radio mediante aprendizaje supervisado

In 5G systems, the Network Slicing (NS) feature allows to deploy several logical networks customized for specific verticals over a common physical infrastructure. In the Radio Access Network (RAN), cellular operators need slice performance models for re-dimensioning purposes. In this work, we present a comprehensive analysis assessing the performance of Supervised Learning (SL) to estimate slice throughput in the down link of RAN-sliced networks, relying on information collected in the operations support system. Different SL algorithms are tested in two NS scenarios with single-service and multi-service slices, respectively. To this end, synthetic datasets with performance indicators and connection traces are generated with a systemlevel simulator emulating the activity of a sliced RAN in a live scenario. Results show that the best model (i.e., combination of SL algorithm and input features) may vary depending on the NS scenario. The best models have shown an error below 10 %.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Herramienta de planificación radio para evaluar la calidad de experiencia en redes móviles

The use of system-level simulators to estimate the impact of self-planning and self-optimization algorithms is a common practice for cellular operators. In this paper, a modification of a 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 based on radio connection traces. For this purpose, an analytical performance model for the scheduling process in a multi-service scenario is developed. The tool is validated with a real trace dataset taken from a live LTE network.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

GPPP y SDR como una potente herramienta científica

One of the greatest problems in mobile networks that researchers have always faced on, is the difficulty of obtaining data from a real network. The limited access to commercial networks or the high prices which presents the equipment encourages the use of simulators in order to get data or test some algorithms. However, these problems can be solved with the emerging of the concept of SDR and GPPP. Hence, in this work it is presented a framework which enables their use in a scientific field. Moreover, a set of video experiment has been made, whose analysis shows the flexibility that these platforms offer as well as its potential as a wide source of real network data, introducing itself in this way, as a powerful tool for researching

Clasificador de celdas de interior en redes celulares.

Las redes móviles desempeñan un papel vital en el mundo actual, basado en la información, en el que las personas dependen cada vez más de ellas en su vida cotidiana. La llegada de las redes 5G ha reforzado esta tendencia, generando nuevos y atractivos servicios, que han provocado un aumento del tráfico celular. Para satisfacer las crecientes demandas de los usuarios, las redes móviles se han vuelto demasiado complejas, lo que hace ineficiente su gestión manual. En este contexto surgen las redes Zero-Touch, que automatizan las tareas de gestión de la red sin intervención humana y con ayuda de la Inteligencia Artificial (IA). Un factor importante para varias decisiones de gestión es el contexto interior/exterior de la celda, aunque este elemento no se registra habitualmente. Este artículo presenta un modelo para la clasificación precisa de celdas interiores utilizando un conjunto de datos reales de Long Term Evolution (LTE). Los resultados obtenidos señalan que los parámetros básicos de configuración son claramente suficientes para determinar el contexto interior de una celda, alcanzando una precisión perfecta en el conjunto de datos de prueba.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Reparto de tráfico en redes 5G con segmentación.

In 5G and beyond wireless systems, Network Slicing (NS) feature will enable the coexistence of extremely different services. In sliced Radio Access Networks (RANs), an optimal traffic sharing among cells is key to guarantee Service Level Agreement (SLA) compliance while minimizing operation costs. The configuration of network functions leading to that optimal point may depend on the slice, claiming for slice-aware traffic steering strategies. This work presents the first data-driven algorithm for slice-aware traffic steering by tuning handover margins. The tuning process is driven by a novel indicator showing the imbalance of SLA compliance among neighbor cells per slice. Performance assessment is carried out with a system-level simulator implementing a realistic sliced RAN offering services with different throughput, latency and reliability requirements. Results show that the proposed algorithm improves the overall SLA compliance by 9% in only 15 minutes of network activity compared to the case of not steering traffic, outperforming a legacy mobility load balancing approachUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Obtención de intervalos de confianza en redes neuronales para predicción en redes celulares

Hoy en día, las redes celulares presentan una complejidad extrema y un alto grado de dinamismo, lo que hace que predecir las fluctuaciones en el rendimiento de la red sea una tarea extremadamente difícil. Gracias a investigaciones anteriores, los modelos de aprendizaje profundo han surgido como una herramienta atractiva para predecir el comportamiento de las redes móviles. Por desgracia, la naturaleza aleatoria del comportamiento de los usuarios en las redes celulares impide una predicción exacta, por lo que conocer el posible error es, en algunos casos, tan importante como la predicción. En este contexto, el intervalo de confianza otorga una valiosa información, definiendo un rango alrededor de la predicción donde se debe encontrar el valor real futuro, con un cierto grado de certidumbre. En este trabajo se presenta un estudio del rendimiento de diferentes modelos de predicción de intervalos de confianza en redes neuronales artificiales. Los resultados destacan a las Redes Bayesianas como una mejor opción que los modelos tradicionales, obteniendo el mismo rendimiento con menor complejidad y tiempo de ejecución para modelar la incertidumbre aleatoria, permitiendo además modelar la componente epistémica.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Filtrado de trazas MDT de alta movilidad mediante aprendizaje supervisado

In beyond 5G networks, geolocated radio information will play a fundamental role to drive self-management algorithms in a zero-touch paradigm. Minimization of Drive Test (MDT) functionality provides operators with geolocated network performance statistics and radio events. However, MDT traces contain important location errors due to energy saving modes, which requires filtering out wrong samples to guarantee an adequate performance of MDT-driven algorithms. In this context, supervised learning (SL) arises as a promising solution to automate the design of MDT filtering procedures compared to rule-based solutions. This work presents a SL-based method to filter MDT measurements in road scenarios by combining user mobility traces and land use maps in the absence of labeled real user mobility traces. Assessment is carried out over real MDT data collected in a live LTE network. Results show that the model performs better in measurements with positioning accuracy information.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec