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

¿En la actualidad le corresponde al demandante probar los elementos estructurales de la responsabilidad estatal en la prestación de servicios médicos?

Procederemos a hacer un rastreo pormenorizado de los diferentes pronunciamientos emitidos por el H. Consejo de Estado y establecer cada uno de los extremos que se han adoptado sobre la materia a lo largo de los años y cuál es el que hoy día prevalece y se aplica a los diferentes procesos de Responsabilidad del Estado por Falla en la prestación de servicios médicos u hospitalarios

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

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