Programa del congreso

The enhancement of virtualization in new generation cellular networks involves the arising of new paradigms in network management. Network Slicing is known as one of the key enablers for the wide range of different high QoS demanding services that are expected to be supported by 5G. The correct sharing of the underlying resources implies a complex architecture that makes virtualization difficult to be full controllable. The Noisy Neighbour is identified as an entity that uses most of the underlying resources, while other virtual units are suffering a lack of them. This work presents an emulated 5G Noisy Neighbour scenario, in which the impact of this entity is evaluated through the Virtual Network Functions of the 5G Core, in order to assess the degradation that KPIs suffer when a Noisy Neighbour appears. The present work also evaluates the effectiveness of a Machine Learning Noisy Neighbour identification model, based on the metrics gathered from the proposed framework, and proposes the application of Artificial Intelligence for predicting network performance, based on network inputs and CPU resources used by the Virtual Network Functions, and a prediction model to forecast the amount of CPU resources that may be demanded by the network in each moment. This approach intends to enhance the resources awareness in a virtualized cellular network, what is posed as crucial for efficiently managing the Noisy Neighbour problem.

Unlike previous generations of mobile communications, 5G is intended to cover a wide variety of scenarios and services with different characteristics and requirements. In this context, the network management team tasks are becoming unattainable. This work develops a system that automatically analyses network data and based on it, recommends cell parameter changes to enhance the performance of the network. A Decision Support System(DSS) that draws the relationship between cell types, cell states and configuration parameters is proposed.

We investigate the impact of a product-channel attack against wireless physical layer security with different diversity techniques. The attack proposed is based on introducing synthetic fading that aims to make the base station transmit at a rate higher than the secrecy capacity. We demonstrate that a low complex transmit antenna selection (TAS) criterion based on the eavesdropper channel improves the robustness against the attack better than the traditional maximal ratio transmission (MRT) scheme or the TAS based on the legitimate channel. Analytical results and simulations are provided to corroborate this fact.

Mobile network users are demanding with regard to the quality of the services, forcing the operators to solve the network degradations in the shortest time possible. For this purpose, a method for root cause diagnosis of degradations is proposed. It is based on different correlations (among CMs and a KPI) and the weighted average of sigma points to provice a ranking of possible causal candidates. Finally, this method is tested on metrics obtained from a commercial equipment.

The use of Time Division Duplex (TDD) has not been fully adopted by operators in LTE networks and its previous generations. In contrast, the fifth generation (5G) is introducing new technical motivations for its use. In order to achieve network flexibility as well as to provide service to every use case, it is necesary to adapt the resources allocated to DL and UL. On the other hand, beamforming techniques require sharing channel state information regularly in both directions. Hence, TDD is a promising option, although it may cause various interference types. The aim of this work is to analyze the Cross-Link Interference (CLI). To do this, a complete scenario simulation has been configured with different conditions, while the signal- to-interference-plus-noise ratio (SINR) has been monitored.