Self-Tuning of Service Priority Parameters for Optimizing Quality of Experience in LTE

Rising user expectations are changing the way mobile operators manage their networks. In this paper, a self-tuning algorithm for adjusting parameters in a multiservice packet scheduler of a Long-Term Evolution base station is proposed to optimize the overall system Quality of Experience (QoE) based on network performance statistics. For this purpose, the algorithm iteratively changes service priority parameters to reprioritize services so as to make the most of available resources. The proposed algorithm ensures that the best overall system QoE is always reached by analyzing optimality conditions, unlike previous works, which only guarantee a minimum user satisfaction level or aim to balance QoE among services. Method assessment is carried out with a dynamic system-level simulator in a realistic service scenario. Simulation results show that the overall network QoE can be improved up to 35% by tuning service priority parameters.Spanish Ministry of Economy and Competitiveness (TEC2015-69982-R) and Optimi-Ericsson and Agencia IDEA (Consejeria de Ciencia, Innovacion y Empresa, Junta de Andalucıa, ref. 59288), co-funded by FEDER

Anomaly detection of High-Mobility MDT Traces Through Self-Supervised Learning.

Radio access network optimization is one of the most critical tasks in cellular systems. For this purpose, Minimization of Drive Test (MDT) functionality provides mobile operators with geolocated network performance statistics to tune radio propagation models in replanning tools. However, MDT traces contain critical location errors due to energy-saving modes, which require filtering out wrong samples to guarantee an adequate performance of MDT-driven algorithms. The design of such a classifier detecting valid measurements can be automated by training a supervised learning model with a labeled dataset. Unfortunately, labeling MDT data is a labor-intensive process. In this context, self-supervised learning (SSL) arises as a promising solution to automate labeling of MDT measurements compared to rulebased solutions. This work presents a novel SSL method to filter MDT measurements in road scenarios by combining user mobility traces constructed with unlabeled MDT data and freely available land-use maps. Once labeled, measurements are used to train a supervised learning model. To this end, a proper set of handcrafted features is first derived. Model assessment is carried out over real MDT data collected in a live Long-Term Evolution (LTE) network. Performance analysis includes well-known supervised models, such as Support-Vector Machine, Random Forest, k-Nearest Neighbors and Multi-Layer Perceptron. Results show that all models perform better in MDT measurements including positioning accuracy information. Nevertheless, it is shown that models without this feature can still be used obtaining reliable results and more generalizable models.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Data-Driven Traffic 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 mobility strategies are required to assign users to the most adequate layer. In this paper, a data-driven self-tuning algorithm for traffic steering is proposed to improve the overall Quality of Experience (QoE) in multi-carrier Long Term Evolution (LTE) networks. Traffic steering is achieved by changing Reference Signal Received Quality (RSRQ)-based inter-frequency handover margins. Unlike classical approaches considering cell-aggregated counters to drive the tuning process, the proposed algorithm relies on a novel indicator, derived from connection traces, showing the impact of handovers on user QoE. 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 obtained with classical load balancing techniques.Spanish Ministry of Economy and Competitiveness under Grant TEC2015-69982-R, in part by the Spanish Ministry of Education, Culture and Sports under FPU Grant FPU17/04286, and in part by the Horizon 2020 Project ONE5G under Grant ICT-76080

A predictive analysis of slice performance in B5G Systems with network slicing

En los sistemas 5G y posteriores, la segmentación de red (Network Slicing, NS) permite la operación simultánea de múltiples redes lógicas personalizadas para sectores verticales específicos sobre una infraestructura física común. En la red de acceso radio, los operadores necesitan prever el rendimiento de los segmentos para una (re)distribución eficiente de los recursos radio entre los mismos. En los últimos años, los modelos basados en el aprendizaje supervisado (Supervised Learning, SL) han mostrado un excelente rendimiento para tareas de predicción en diversos campos. Aun así, un análisis preliminar de las series temporales de indicadores de rendimiento (Key Performance Indicators, KPIs) a nivel de segmento es clave para seleccionar el predictor basado en SL óptimo. Este trabajo presenta un juego de datos de KPI a nivel de segmento creado con un simulador dinámico que emula una red de acceso de radio 5G realista con NS. El juego de datos incluye medidas históricas de varios KPI recopilados por célula y segmento durante 15 minutos de actividad de la red. Sobre él, se realiza un análisis de correlación cruzada, auto correlación y estacionalidad, con el objetivo de caracterizar las series temporales de KPIs recopilados a nivel de segmento. Los resultados han mostrado que algunos aspectos clave para el diseño de modelos de predicción (por ejemplo, el comportamiento estacional, la predictibilidad o la correlación entre distintos KPIs) dependen en gran medida de ambos la resolución temporal de los datos y del segmento. Se espera que modelos de predicción multi-KPI con detección automática de estacionalidad entrenados específicamente para cada segmento consigan los mejores resultados.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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

Improving lifespan automation for Caenorhabditis elegans by using image processing and a post-processing adaptive data filter

[EN] Automated lifespan determination for C. elegans cultured in standard Petri dishes is challenging. Problems include occlusions of Petri dish edges, aggregation of worms, and accumulation of dirt (dust spots on lids) during assays, etc. This work presents a protocol for a lifespan assay, with two image-processing pipelines applied to different plate zones, and a new data post-processing method to solve the aforementioned problems. Specifically, certain steps in the culture protocol were taken to alleviate aggregation, occlusions, contamination, and condensation problems. This method is based on an active illumination system and facilitates automated image sequence analysis, does not need human threshold adjustments, and simplifies the techniques required to extract lifespan curves. In addition, two image-processing pipelines, applied to different plate zones, were employed for automated lifespan determination. The first image-processing pipeline was applied to a wall zone and used only pixel level information because worm size or shape features were unavailable in this zone. However, the second image-processing pipeline, applied to the plate centre, fused information at worm and pixel levels. Simple death event detection was used to automatically obtain lifespan curves from the image sequences that were captured once daily throughout the assay. Finally, a new post-processing method was applied to the extracted lifespan curves to filter errors. The experimental results showed that the errors in automated counting of live worms followed the Gaussian distribution with a mean of 2.91% and a standard deviation of +/- 12.73% per Petri plate. Post-processing reduced this error to 0.54 +/- 8.18% per plate. The automated survival curve incurred an error of 4.62 +/- 2.01%, while the post-process method reduced the lifespan curve error to approximately 2.24 +/- 0.55%.This study was also supported by the CDTI agency of the Spanish Ministry of Economy and Competitiveness with CIEN project SMARTFOODS, Universitat PolitAcnica de Valencia with Project 20170020-UPV, Plan Nacional de I + D with Project RTI2018-094312-B-I00 and by European FEDER funds. ADM Nutrition, Biopolis SL and Archer Daniels Midland provided support in the form of salaries for authors P. M. Guerola and S. G. Martinez.Puchalt-Rodríguez, JC.; Sánchez Salmerón, AJ.; Ivorra Martínez, E.; Genovés Martínez, S.; Martínez, R.; Martorell Guerola, P. (2020). Improving lifespan automation for Caenorhabditis elegans by using image processing and a post-processing adaptive data filter. Scientific Reports. 10(1):1-14. https://doi.org/10.1038/s41598-020-65619-4114101Brenner, S. The Genetics Of Caenorhabditis Elegans. 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Coffee silverskin extract protects against accelerated aging caused by oxidative agents

Nowadays, coffee beans are almost exclusively used for the preparation of the beverage. The sustainability of coffee production can be achieved introducing new applications for the valorization of coffee by-products. Coffee silverskin is the by-product generated during roasting, and because of its powerful antioxidant capacity, coffee silverskin aqueous extract (CSE) may be used for other applications, such as antiaging cosmetics and dermaceutics. This study aims to contribute to the coffee sector’s sustainability through the application of CSE to preserve skin health. Preclinical data regarding the antiaging properties of CSE employing human keratinocytes and Caenorhabditis elegans are collected during the present study. Accelerated aging was induced by tert-butyl hydroperoxide (t-BOOH) in HaCaT cells and by ultraviolet radiation C (UVC) in C. elegans. Results suggest that the tested concentrations of coffee extracts were not cytotoxic, and CSE 1 mg/mL gave resistance to skin cells when oxidative damage was induced by t-BOOH. On the other hand, nematodes treated with CSE (1 mg/mL) showed a significant increased longevity compared to those cultured on a standard diet. In conclusion, our results support the antiaging properties of the CSE and its great potential for improving skin health due to its antioxidant character associated with phenols among other bioactive compounds present in the botanical materialThe authors are grateful for the financial support from the SUSCOFFEE Project (AGL2014-57239-R) and the NATURAGE Project (AGL2010-17779). This work was partially funded by a Santander Small and Medium Enterprises Work Placement Grant in Beacon Biomedicine. Amaia Iriondo is a fellow of the FPI predoctoral program of the Ministry of Economy and Competitiveness (BES-2015-072191). Konstantinos Stamatakis is a recipient of an Asociación Española Contra el Cancer fellowship.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

Big Data Analytics for Automated QoE Management in Mobile Networks

Over the last years, there has been a significant increase in the number of services in mobile networks. This trend has forced operators to change their network management processes to ensure adequate user QoE, instead of adequate QoS. As a result, customer experience management is now a critical task for mobile network operators, who demand tools for QoE monitoring on an individual user basis. With the latest advances in information technologies, the newest TMA solutions can leverage the huge amount of information available from network elements and interfaces in mobile networks. However, data processing algorithms in these tools are still to be defined. In this work, we review the shortcomings and challenges in the use of TMA applications in mobile networks, and how these can be empowered by big data analytics. For this purpose, a methodology to validate a generic big-data-driven TMA framework with user terminal agents in a real cellular network is outlined. A use case is presented to show the potential and limitations of these applications for monitoring end-user QoE in a live LTE network.Spanish Ministry of Economy and Competitiveness (TEC2015-69982-R) and Ericsson Spain

Gut microbial composition in patients with psoriasis

Since the last 5–10 years the relevance of the gut microbiome on different intestinal illnesses has been revealed. Recent findings indicate the effect of gut microbiome on certain dermatological diseases such as atopic dermatitis. However, data on other skin diseases such as psoriasis are limited. This is the first time attempting to reveal the gut microbiome composition of psoriatic patients with a prospective study including a group of patients with plaque psoriasis, analyzing their gut microbiome and the relationship between the microbiome composition and bacterial translocation. The microbiome of a cohort of 52 psoriatic patients (PASI score ≥6) was obtained by 16s rRNA massive sequencing with MiSeq platform (Illumina inc, San Diego) with an average of 85,000 sequences per sample. The study of the gut microbiome and enterotype shows from the first time a specific “psoriatic core intestinal microbiome” that clearly differs from the one present in healthy population. In addition, those psoriatic patients classified as belonging to enterotype 2 tended to experience more frequent bacterial translocation and higher inflammatory status (71%) than patients with other enterotypes (16% for enterotype 1; and 21% for enterotype 3).Medicin