Programa del congreso

Both conventional Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) and Discrete Fourier Transform Spread OFDM (DFT-S-OFDM) have been adopted for their use in the Physical Uplink Shared Channel (PUSCH) in the 5G New Radio (NR) standard. While CP-OFDM can better exploit the frequency characteristics of the channel, DFT-S-OFDM has the advantage of a lower peak-to-average power ratio (PAPR). Due to the interactions between PAPR and power amplifier (PA) non-linearity, users using DFT-S-OFDM waveform may benefit from a potentially higher PA efficiency and extend their coverage by increasing their transmit power. In this paper we study the uplink performance of both waveforms and their interaction with non-linear PA and uplink power control in the millimeter-wave (mmWave) band to determine their optimal operational range.

In the 3GPP LTE Release 13, NB-IoT was standardized to provide wide-area connectivity for IoT. To optimize network signalling and power consumption, control plane (CP) and user plane (UP) optimizations were introduced. Also, to support infrequent small data transmissions, in Release 15 Early Data Transmissions (EDT) was introduced, where the data is multiplexed with NAS signalling. Therefore, this paper analyses the latency performance of the different NB-IoT optimizations. The study, which has been carried out in a simulated cellular factory environment, has been performed for different packet sizes. Evaluation results show that with low packet size, EDT with CP provides lower latency. However, with higher packet sizes, user plane solutions provide better latency.

Mobile networks have gained importance in recent years thanks to the massive use of the Internet in the mobile terminal. It is very useful to know the impact on the network when any type of event occurs in the city to avoid failures in its operation due to the high attendance of the areas where the event occurs. That is why, in this work the impact of different mobility models on the cellular network is studied, as well as determining which of these models are the most optimal and realistic in relation to the study of a real scenario. Consequently, mobility models are developed on a simulation environment where users move through the areas near the event. For its evaluation, comparative results are obtained respect to the behavior of the number of users per cell for each mobility model, SINR variation and the trade-off between computational cost and mobility model complexity.

High-precision indoor location has become a necessity for the new location-based services that are emerging around 5G. The European LOCUS project is a research project that seeks to achieve high-precision location based on the 5G network. This work reflects one of the proof-of-concepts of theLOCUS project in which we propose the opportunistic fusion ofdifferent technologies, such as Ultra Wide Band (UWB) and WiFi Fine Time Measurement (FTM), to improve location accuracy.For this purpose, an experimental setup has been settle to validate the presented system, using both technologies due to their incorporation in the latest smartphones on the market. In this way, the use of fusion in trilateration is validated as an algorithm that significantly improves the positioning error by overdetermining the localization problem and also improves the coverage area.

La creciente complejidad de las redes móviles, aun más con el desarrollo del 5G, dificulta la relación de las tradicionales métricas radio de capas bajas utilizadas hasta ahora, con la calidad real del usuario. Estas medidas conocidas como KPI (Key Performance Indicator) son fáciles de obtener en comparación con los KQI (Key Quality Indicator), pero son estos últimos, los que dan una aproximación real a la calidad del usuario. El problema de los KQI es la dificultad de obtención, consumen tiempo y batería en los dispositivos móviles, ya que la amplia mayoría de ellos requieren de la descarga y subida de archivos, peticiones http, transmisión en directo de video, entre otros. Se propone y evalúa en una red celular real, un marco completo para estimar KQI utilizando KPI e información adicional, consiguiendo las ventajas de precisión de los KQI a partir de la facilidad de obtención de los KPI.