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

Asignación de cabezales radio a procesadores banda base mediante redes neuronales de grafos.

In 5G networks, Cloud-Radio Access Network (C-RAN) architecture divides legacy base stations into Radio Remote Heads (RRH) and Base Band Units (BBU). RRHs transmit and receive radio signals, whereas BBUs process those signals. Thus, BBUs can be centralized in cloud processing centers serving different groups of RRHs. An adequate allocation of RRHs to BBUs is essential to guarantee C-RAN performance. With the latest advances in machine learning, this task can be automatically addressed through supervised learning. This paper proposes a methodology for allocating RRHs to BBUs in heterogeneous cellular networks relying on graph partitioning through a graph neural network. Model performance is assessed over a dataset built with a radio planning tool that implements a realistic Long-Term Evolution (LTE) heterogeneous network. Results have shown that the proposed method improves performance of a patented state-of-theart tool based on graph partitioning.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Predicción del rendimiento en redes celulares con segmentación.

In 5G and beyond systems, Network Slicing (NS) enables the deployment of multiple logical networks customized for specific verticals over a common physical infrastructure. In the radio access network, mobile operators need models to forecast slice performance for an efficient and proactive slice redimensioning. This task has not been addressed yet due to the absence of public datasets from live 5G networks with NS comprising historical measurements of Key Performance Indicators (KPIs) collected on a slice basis to test on. This work presents, a slice-level KPI dataset created with a dynamic system-level simulator that emulates the activity of a realistic 5G network with NS. The dataset comprises historical measurements for several KPIs collected per cell and slice for 15 minutes of network activity. Then, a thorough analysis of the dataset is presented considering correlation- and seasonality-related features, aiming to characterize slice-level KPI time series for different slices and data aggregation resolutions. Results have shown that some key aspects for designing slice-level forecasting models (e.g., seasonal KPI behavior or relationship among KPIs) strongly depend on slice and data time resolution. Slice-specific multi-KPI forecasting models with automatic seasonality detection are expected to achieve the best performanceUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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

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

Research Group on Earth Observation, Geological Risks and Climate Change (OBTIER)

[EN] Within the framework of the IGME-CSIC Department of Geological Hazards and Climate Change, the OBTIER research group was created in July 2021 and currently has 22 members, including scientific and technical staff, as well as young people with contracts linked to competitive national and international research projects. The main objective of the group is to provide society with scientific information, methods, tools and solutions to mitigate the impact of geohazards and the effects of Climate Change. OBTIER is currently leading 6 competitive projects (4 European and 2 national), as well as several projects in agreement with other national and international administrations. It is an active member of the EuroGeoSurveys Earth Observation Expert Group and the ASGMI Geological Hazards Group. OBTIER offers society a wide range of capabilities on: earthquakes, tsunamis, landslides, land subsidence, volcanic eruptions, droughts and floods. In 2021, the group published an article in Science entitled: Mapping the global threat of land subsidence with significant media coverage around the world.Peer reviewe

Spatiotemporal Characteristics of the Largest HIV-1 CRF02_AG Outbreak in Spain: Evidence for Onward Transmissions

Background and Aim: The circulating recombinant form 02_AG (CRF02_AG) is the predominant clade among the human immunodeficiency virus type-1 (HIV-1) non-Bs with a prevalence of 5.97% (95% Confidence Interval-CI: 5.41–6.57%) across Spain. Our aim was to estimate the levels of regional clustering for CRF02_AG and the spatiotemporal characteristics of the largest CRF02_AG subepidemic in Spain.Methods: We studied 396 CRF02_AG sequences obtained from HIV-1 diagnosed patients during 2000–2014 from 10 autonomous communities of Spain. Phylogenetic analysis was performed on the 391 CRF02_AG sequences along with all globally sampled CRF02_AG sequences (N = 3,302) as references. Phylodynamic and phylogeographic analysis was performed to the largest CRF02_AG monophyletic cluster by a Bayesian method in BEAST v1.8.0 and by reconstructing ancestral states using the criterion of parsimony in Mesquite v3.4, respectively.Results: The HIV-1 CRF02_AG prevalence differed across Spanish autonomous communities we sampled from (p < 0.001). Phylogenetic analysis revealed that 52.7% of the CRF02_AG sequences formed 56 monophyletic clusters, with a range of 2–79 sequences. The CRF02_AG regional dispersal differed across Spain (p = 0.003), as suggested by monophyletic clustering. For the largest monophyletic cluster (subepidemic) (N = 79), 49.4% of the clustered sequences originated from Madrid, while most sequences (51.9%) had been obtained from men having sex with men (MSM). Molecular clock analysis suggested that the origin (tMRCA) of the CRF02_AG subepidemic was in 2002 (median estimate; 95% Highest Posterior Density-HPD interval: 1999–2004). Additionally, we found significant clustering within the CRF02_AG subepidemic according to the ethnic origin.Conclusion: CRF02_AG has been introduced as a result of multiple introductions in Spain, following regional dispersal in several cases. We showed that CRF02_AG transmissions were mostly due to regional dispersal in Spain. The hot-spot for the largest CRF02_AG regional subepidemic in Spain was in Madrid associated with MSM transmission risk group. The existence of subepidemics suggest that several spillovers occurred from Madrid to other areas. CRF02_AG sequences from Hispanics were clustered in a separate subclade suggesting no linkage between the local and Hispanic subepidemics

Data-Driven Self-Management of Cellular Radio Access Networks.

Cabe destacar que las herramientas de autogestión orientadas al servicio requieren conocer a priori el servicio demandado por cada usuario. Sin embargo, en la actualidad esta información no es fácil de obtener debido al encriptado del tráfico. La clasificación de tráfico encriptado por tipo de servicio también se aborda en esta tesis.En la actualidad, las redes de comunicaciones móviles están experimentando grandes cambios para hacer frente a la creciente demanda de servicios móviles. Como resultado, el tamaño y la complejidad de estas redes ha crecido dramáticamente, evidenciando la necesidad de soluciones de gestión de red sin intervención humana. En la red de acceso radio, los operadores ya han abordado la automatización de los procedimientos de gestión en el pasado, dando lugar a las redes autoorganizadas. Sin embargo, las soluciones clásicas no serán efectivas en las redes de nueva generación que ofrecen servicios con requisitos de rendimiento extremadamente exigentes y diversos. Con los últimos avances en tecnologías de la información, se puede aprovechar la ingente cantidad de datos almacenados en el sistema de soporte a las operaciones de la red para desarrollar herramientas de gestión automática avanzadas basadas en datos, capaces de capturar las peculiaridades de cada red particular. Estas nuevas soluciones de gestión automática deben tener en cuenta las nuevas funcionalidades que surgen en 5G, como por ejemplo la segmentación de red. Esta tesis aborda la creación de herramientas basadas en el uso intensivo de datos para dos casos de uso de autoplanificación y autooptimización muy extendidos: el redimensionado de la red de acceso radio y el balance de tráfico por movilidad. En ambos casos, se proponen soluciones para las redes radio clásicas, en las que los recursos se comparten por todos los usuarios, y para las nuevas redes de acceso radio segmentadas que aparecen en 5G