Coordination and load analysis of C-RAN in HetNets by graph-partitioning

In 5G systems, ultra-dense networks are a promising technique to cope strong increase of traffic data in mobile communications. In addition, the deployment of indoor small cells offloads the wireless system from macrocells at the cost of increasing network complexity. In this work, a method for capacity analysis of Centralized Radio Access Networks (C-RANs) comprising macrocells and small cells is proposed. Radio remote heads~(RRH) are grouped to a Base Band Unit~(BBU) pools using graph theory techniques. For this purpose, the impact of Inter-Cell Interference Coordination (ICIC) and Coordinated Multi-Point Transmission/Reception (CoMP) techniques on the network is assessed under different load levels and coordination restrictions. Assessment is carried out by using a radio planning tool that allows to characterize spectral efficiency and allocation of shared resources per cell over a realistic Long-Term Evolution (LTE) heterogeneous network. Results show that load and coordination conditions between cells are key to improve system capacity.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Evaluación de la calidad de experiencia de YouTube Live en redes inalámbricas

[EN] YouTube Live is one of the most popular services on the Internet, enabling an easy streaming of a live video with acceptable video quality. Thus, understanding user´s perception of this service is of the utmost importance for network operators. As in other videostreaming services, YouTube Live traffic is sometimes affected by delays due to unfavourable network conditions, which translate into unacceptable initial reproduction times or image freezes as a result of client´s buffer underrun. Detecting these events is key to ensure an adequate Quality of experience (QoE). Unfortunately, data encryption makes it very difficult for operators to monitor QoE from packet-level data collected in network interfaces. In this paper, an analytical model to estimate the QoE for encrypted YouTube Live service from packet-level data collected in the interfaces of a wireless network is presented. The inputs to the model are Transport Control Protocol (TCP)/Internet Protocol (IP) metrics, from which three Service Key Performance Indicators (S-KPIs) are estimated, namely initial video play start time, video interruption duration and video interruption. The model is developed with an experimental platform, consisting of a user terminal agent, a WiFi wireless network, a network-level emulator and a probe software. Model assessment is carried out by comparing S-KPI estimates with measurements from the terminal agent under different network conditions introduced by the network emulator.Este trabajo ha sido financiado por el Ministerio de Economía y Competitividad (Proyecto TEC2015-69982-R, UNMA13-1E-1864), y FEDER.Jimenez, L.; Solera, M.; Toril, M.; Oliver, P. (2018). Evaluación de la calidad de experiencia de YouTube Live en redes inalámbricas. En XIII Jornadas de Ingeniería telemática (JITEL 2017). Libro de actas. Editorial Universitat Politècnica de València. 233-240. https://doi.org/10.4995/JITEL2017.2017.6611OCS23324

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

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

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

Analysis of heuristic graph partitioning methods for the assignment of packet control units in GERAN

Over the last few years, graph partitioning has been recognized as a suitable technique for optimizing cellular network structure. For example, in a recent paper, the authors proposed a classical graph partitioning algorithm to optimize the assignment of cells to Packet Control Units (PCUs) in GSM-EDGE Radio Access Network. Based on this approach, the quality of packet data services in a live environment was increased by reducing the number of cell re-selections between different PCUs. To learn more about the potential of graph partitioning in cellular networks, in this paper, a more sophisticated, yet computationally efficient, partitioning algorithm is proposed for the same problem. The new method combines multi-level refinement and adaptive multi-start techniques with algorithms to ensure the connectivity between cells under the same PCU. Performance assessment is based on an extensive set of graphs constructed with data taken from a live network. During the tests, the new method is compared with classical graph partitioning approaches. Results show that the proposed method outperforms classical approaches in terms of solution quality at the expense of a slight increase in computing time, while providing solutions that are easier to check by the network operator