Potentzia domeinuko NOMA 5G sareetarako eta haratago

Tesis inglés 268 p. -- Tesis euskera 274 p.During the last decade, the amount of data carried over wireless networks has grown exponentially. Several reasons have led to this situation, but the most influential ones are the massive deployment of devices connected to the network and the constant evolution in the services offered. In this context, 5G targets the correct implementation of every application integrated into the use cases. Nevertheless, the biggest challenge to make ITU-R defined cases (eMBB, URLLC and mMTC) a reality is the improvement in spectral efficiency. Therefore, in this thesis, a combination of two mechanisms is proposed to improve spectral efficiency: Non-Orthogonal Multiple Access (NOMA) techniques and Radio Resource Management (RRM) schemes. Specifically, NOMA transmits simultaneously several layered data flows so that the whole bandwidth is used throughout the entire time to deliver more than one service simultaneously. Then, RRM schemes provide efficient management and distribution of radio resources among network users. Although NOMA techniques and RRM schemes can be very advantageous in all use cases, this thesis focuses on making contributions in eMBB and URLLC environments and proposing solutions to communications that are expected to be relevant in 6G

Beamforming adaptativo basado en Deep Reinforcement Learning para comunicaciones IBFD (In-Band Full-Duplex)

El continuo crecimiento´de contenidos a través de los actuales sistemas de radiodifusión, hacen necesaria una evolución hacia nuevas tecnologías que puedan cubrir las futuras necesidades. En el panorama de la televisión digital, el comité ATSC 3.0, propone una nueva arquitectura, IDL/ITCN, que permita realizar la convergencia hacia lo que denominan como la próxima generación de televisión digital. Sin embargo, estas nuevas tecnologías incorporan nuevos retos, como la gestión de una gran cantidad de señales interferentes. Dentro de este contexto, este proyecto tiene como objetivo establecer unas bases iniciales hacia lo que derivaría en una investigación mayor, la cual pueda facilitar la gestión de las señales de interferencia dentro de estos nuevos escenarios. Para ello, se propone una solución que combina las actuales técnicas para la gestión de interferencias, con algoritmos de machine learning. De esta forma se pretende obtener una solución más eficiente que la conseguida con los actuales sistemas.The continuous growth in content delivery through the current broadcasting systems makes necessary the evolution towards new technologies that can address future needs. In terms of digital television, the ATSC 3.0 committee proposes a new architecture, IDL/ITCN, to enable convergence towards the next generation of digital television. However, these new technologies incorporate new challenges, such as managing a large number of interfering signals. In this context, this project aims to establish the initial basis for further research to facilitate the management of interference signals within these new scenarios. For this purpose, we proposed a solution that combines current interference management techniques with machine learning algorithms. In this way, it is intended to obtain a more optimal solution than the one achieved with the traditional systems.Egungo irrati-difusio sistemen bidez pairatu den eduki-hornikuntzaren gorakadak teknologia berrietaranzko bilakaera bat eskatzen du, egoera berri honek sortu dituen beharrei erantzun ahal izateko. Telebista digitalaren alorrean, ATSC 3.0 batzordeak arkitektura berri bat proposatzen du hurrengo belaunaldiarekiko konbergentzia gauzatu ahal izateko, IDL/ITCN bezala ezagutzen dena. Hala ere, teknologia berri horiek erronka berriak eskatzen dituzte, hala nola interferentzia-seinale askoren kudeaketa. Testuinguru horren barruan, proiektu honen helburua hasierako oinarriak ezartzea da, gerora, ikerketa handiago bat ekarriko lukeena egoera berri horien barruan interferentzia-seinaleen kudeaketa errazteko. Horretarako, interferentziak kudeatzeko metodo tradizionalak eta machine learning algoritmoak konbinatu nahi dira, egungo sistemekin lortutakoa baino irtenbide hobea lortzea ahalbidetuko dutenak