Modelling and Optimisation of GSM and UMTS Radio Access Networks

The size and complexity of mobile communication networks have increased in the last years making network management a very complicated task. GSM/EDGE Radio Access Network (GERAN) systems are in a mature state now. Thus, non-optimal performance does not come from typical network start-up problems, but, more likely, from the mismatching between traffic, network or propagation models used for network planning, and their real counterparts. Such differences cause network congestion problems both in signalling and data channels. With the aim of maximising the financial benefits on their mature networks, operators do not solve anymore congestion problems by adding new radio resources, as they usually did. Alternatively, two main strategies can be adopted, a) a better assignment of radio resources through a re-planning approach, and/or b) the automatic configuration (optimisation, in a wide sense) of network parameters. Both techniques aim to adapt the network to the actual traffic and propagation conditions. Moreover, a new heterogenous scenario, where several services and Radio Access Technologies (RATs) coexist in the same area, is now common, causing new unbalanced traffic scenarios and congestion problems. In this thesis, several optimisation and modelling methods are proposed to solve congestion problems in data and signalling channels for single- and multi-RAT scenarios

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

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

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 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

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

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