In the sixth generation of wireless communication systems (6G), there exist multiple candidate enabling technologies that help the wireless network satisfy the ever-increasing demand for speed, coverage, reliability, and mobility. Among these technologies, reconfigurable intelligent surfaces (RISs) extend the coverage of a wireless network into dead zones, increase capacity, and facilitate integrated sensing and communications tasks by consuming very low power, thus contributing to energy efficiency as well. RISs are meta-material-based devices whose electromagnetic reflection characteristics can be controlled externally to cater to the needs of the communication links. Most ubiquitously, this comes in the form of adding a desired phase shift to an incident wave before reflecting it, which can be used to phase-align multiple incident waves to increase the strength of the signal at the receiver and provide coverage to an area that otherwise would be a dead zone. While this portrays an image of a dream technology that would boost the existing wireless networks significantly, RISs do not come without engineering problems. First of all, the individual elements do not exhibit ideal reflection characteristics, that is, they attenuate the incident signal in a fashion depending on the configured phase shift. This creates the phenomenon called "phase-dependent amplitude". Another problem caused by RISs is the channel estimation overhead. In a multiple-antenna communication system, the channel between two terminals is as complex as the product of the number of antennas at each end. However, when an RIS comes into the equation, the cascade of the transmitter-RIS and RIS-receiver channels has a complexity further multiplied by the number of RIS elements. Consequently, the channel estimation process to utilize the RIS effectively becomes more demanding, that is, more pilot signals are required to estimate the channel for coherent reception. This adversely affects the effective data rate within a communication system since more resources need to be spent for pilot transmission and fewer resources can be allocated for data transmission. While there exists some work on reducing the channel dimensions by exploiting the channel structure, this problem persists for unstructured channels. In addition, for the wireless networks using multiple RISs, a new kind of pilot contamination arises, which is the main topic of this thesis. In the first part of this thesis, we study this new kind of pilot contamination in a multi-operator context, where two operators provide services to their respective served users and share a single site. Each operator has a single dedicated RIS and they use disjoint frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration. In the second part of this thesis, we consider a single-operator-two-RIS integrated sensing and communication (ISAC) system where the single user is both a communication terminal and a positioning target. Based on the uplink positioning pilots, the base station aims to estimate both the communication channel and the user's position within the indoor environment by estimating the angle of arrival (AoA) of the impinging signals on both RISs and then exploiting the system and array geometries to estimate the user position and user channels respectively. Although there is a single operator, due to the presence of multiple RISs, pilot contamination occurs through the same physical means as multi-operator pilot contamination unless the channel estimation process is parameterized. Since the communication links are considered to be pure line-of-sight (LOS), their structure allows the reduction of the number of unknown parameters. Consequently, the reduction of information caused by pilot contamination does not affect the channel estimation procedure, hence the pilot contamination is overcome. On the other hand, the position of the user is determined by intersecting the lines drawn along the AoA estimates. We adopt the Cramér-Rao Lower Bound (CRLB), the lower bound on the mean squared error (MSE) of any unbiased estimator, for both channel estimation and positioning. Our numerical results show that it is possible to utilize positioning pilots for parametric channel estimation when the wireless links are LOS. / <p>QC 20240416</p>

[ES] El aprovechamiento de la tecnología de Quinta Generación (5G) para impulsar a la Industria 4.0 ha marcado un hito significativo en la evolución histórica de las redes celulares. Este desarrollo tiene como objetivo respaldar a las fábricas inteligentes con estrictos requisitos de comunicación, ya que su operatividad se centra en cumplir con los estándares de Calidad de Servicio (QoS), lo que hace que las aplicaciones del Industrial Internet de las cosas (IIoT) sean susceptibles a un rendimiento de red inestable. Además, estas aplicaciones suelen ocurrir en interiores, donde la alta densidad de obstáculos presenta desafíos adicionales. Estructuras metálicas grandes, robots y vehículos en movimiento obstruyen la propagación de la señal y pueden degradar significativamente el rendimiento de las comunicaciones. El primer modelo de canal estandarizado para fábricas interiores (InF) fue introducido por el Third Generation Partnership Project (3GPP) en la Release 16 para estudiar y abordar estas particularidades ambientales. Esta tesis se centra en esta base y examina el procedimiento de modelado, identificando limitaciones como la caracterización imprecisa de parámetros y la capacidad limitada para capturar toda la complejidad geométrica de tales entornos. Preocupado por estas limitaciones, este trabajo da un paso significativo hacia adelante al proponer una nueva tecnología para abordar los desafíos en el modelado industrial. Este enfoque abre la puerta a explorar una de las tendencias emergentes clave en la Sexta Generación (6G) para aplicaciones IIoT: los sistemas de Integrated Sensing and Communications (ISAC). Los sistemas ISAC tienen un gran potencial para superar no solo los desafíos existentes, sino también para introducir mejoras adicionales y valiosas. Dado que ISAC es una tecnología novedosa, aún no se ha diseñado un modelo de canal específico para ella. Para cubrir esta necesidad, esta tesis presenta el desarrollo de un modelo de canal ISAC como un paso fundamental para avanzar en esta tecnología. Durante dicho avance, se han identificado características fundamentales para construir un modelo de canal ISAC, las cuales suelen ser pasadas por alto en la literatura. En respuesta a esto, este trabajo motiva el desarrollo de directrices técnicas para el modelado ISAC, formando una metodología de evaluación. Una metodología de evaluación es importante para ISAC o cualquier sistema, ya que es esencial para evaluar el rendimiento y orientar futuras mejoras. Actualmente, no existe una metodología de este tipo para ISAC. Esta tesis aborda estos desafíos al enfatizar la importancia de considerar las características principales para construir un canal ISAC: correlación entre el canal de sensado y el de comunicación y consistencia espacial. Basándose en el desarrollo inicial del marco ISAC, el siguiente paso consiste en probar ISAC en entornos cuasi-realistas. Esta tesis presenta un caso de uso industrial que aplica entrenamiento de haz asistido por sensado, demostrando cómo ISAC puede abordar el problema de las múltiples obstrucciones en tales entornos. Específicamente, explora la técnica de sustracción de fondo en un algoritmo de formación de haces predictiva, que aprovecha la información relacionada con el usuario obtenida a través del sensado. Bajo estas consideraciones, los hallazgos indican una mejora sustancial en el rendimiento de la comunicación, particularmente en lo que respecta a la relación señal a ruido (SNR) y la tasa de datos efectiva. En otras palabras, los resultados destacan el potencial de ISAC para abordar eficazmente las complejidades geométricas del entorno de interés. Esta tesis no solo es pionera en la técnica de sustracción de fondo, sino que también muestra su impacto, allanando el camino para futuras aplicaciones a otros algoritmos de sensado dentro del marco ISAC y la fábrica del futuro. / [CA] L'aprofitament de la tecnologia de Cinquena Generació (5G) per a impulsar la Indústria 4.0 ha marcat un fita significativa en l'evolució històrica de les xarxes cel·lulars. Aquest desenvolupament té com a objectiu donar suport a les fàbriques intel·ligents amb estrictes requisits de comunicació, ja que el seu funcionament depén de complir amb els estàndards de Qualitat de Servei (QoS), cosa que fa que les aplicacions de l'Internet Industrial de les Coses (IIoT) siguen susceptibles a un rendiment de xarxa inestable. A més, aquestes aplicacions solen produir-se en interiors, on la gran densitat d'obstacles presenta desafiaments addicionals. Grans estructures metàl·liques, robots i vehicles en moviment obstrueixen la propagació del senyal i poden degradar significativament el rendiment de les comunicacions. El primer model de canal estandarditzat per a fàbriques interiors (InF) va ser introduït pel Third Generation Partnership Project (3GPP) en la Release 16 per a estudiar i abordar aquestes particularitats ambientals. Aquesta tesi es centra en aquesta base i examina el procediment de modelatge, identificant limitacions com ara la caracterització imprecisa dels paràmetres i la capacitat limitada per a captar tota la complexitat geomètrica d'aquests entorns. Preocupat per aquestes limitacions, aquest treball fa un pas significatiu cap endavant en proposar una nova tecnologia per a abordar els desafiaments en el modelatge industrial. Aquest enfocament obri la porta a explorar una de les tendències emergents clau en la Sisena Generació (6G) per a aplicacions IIoT: els sistemes d'Integrated Sensing and Communications (ISAC). Els sistemes ISAC tenen un gran potencial per a superar no sols els desafiaments existents, sinó també per a introduir millores addicionals i valuoses. Com que ISAC és una tecnologia innovadora, encara no s'ha dissenyat un model de canal específic per a ella. Per a cobrir aquesta necessitat, aquesta tesi presenta el desenvolupament d'un model de canal ISAC com un pas fonamental per a avançar en aquesta tecnologia. En el marc d'aquest avanç, s'han identificat característiques fonamentals per a construir un model de canal ISAC, les quals solen ser passades per alt en la literatura. En resposta a això, aquest treball motiva el desenvolupament de directrius tècniques per al modelatge ISAC, formant una metodologia d'avaluació. Una metodologia d'avaluació és important per a ISAC o per a qualsevol sistema, ja que és essencial per avaluar el rendiment i orientar futures millores. Actualment, no existeix una metodologia d'aquest tipus per a ISAC. Aquesta tesi aborda aquests desafiaments en destacar la importància de considerar les característiques principals per a construir un canal ISAC: correlació entre el canal de sensat i el de comunicació i consistència espacial. Basant-se en el desenvolupament inicial del marc ISAC, el pas següent consisteix a provar ISAC en entorns quasi-realistes. Aquesta tesi presenta un cas d'ús industrial que aplica un entrenament de feix assistit per sensat, demostrant com ISAC pot abordar el problema de les múltiples obstruccions en aquests entorns. Específicament, explora la tècnica de sostracció de fons en un algoritme de formació de feixos predictiva, que aprofita la informació relacionada amb l'usuari obtinguda a través del sensat. Dins d'aquestes consideracions, les troballes indiquen una millora substancial en el rendiment de la comunicació, particularment pel que fa a la relació senyal-soroll (SNR) i la taxa de dades efectiva. En altres paraules, els resultats destaquen el potencial d'ISAC per a abordar eficaçment les complexitats geomètriques de l'entorn d'interés. Aquesta tesi no sols és pionera en la tècnica de sostracció de fons, sinó que també mostra el seu impacte, obrint el camí a futures aplicacions en altres algoritmes de sensat dins del marc ISAC i la fàbrica del futur. / [EN] Leveraging Fifth Generation (5G) technology to advance Industry 4.0 has marked a significant milestone in the historical evolution of cellular networks. This development aims to support smart factories with stringent communication requirements, as their operation is focused on meeting Quality of Service (QoS) standards, making the Industrial Internet of Things (IIoT) applications susceptible to unstable network performance. Moreover, these applications frequently occur indoors, where high-density clutter poses additional challenges. Large metal structures, robots, and moving vehicles obstruct signal propagation and can significantly degrade communication performance. The first standardized channel model for Indoor Factory (InF) was introduced by the Third Generation Partnership Project (3GPP) in Release 16 to study and address these environmental particularities. This Thesis builds on this foundation and examines the modeling procedure, identifying limitations such as imprecise parameter characterization and a limited ability to capture the full geometric complexity of such environments. Concerned about these limitations, this work takes a significant step forward by proposing a new technology to address challenges in industrial modeling. This approach opens the door to exploring one of the key emerging trends in Sixth Generation (6G) for IIoT applications: Integrated Sensing and Communications (ISAC) systems. ISAC systems hold the promising potential to overcome not only existing challenges but also introduce additional, valuable enhancements. As ISAC is a novel technology, no channel model has been specifically designed for it so far. To fill this need, this Thesis presents the development of an ISAC channel model as a foundational step in advancing this technology. During this progress, fundamental features for building an ISAC channel model have been identified, which are often overlooked in the literature. In response, this work motivates the development of technical guidelines for ISAC modeling, forming an evaluation methodology. An evaluation methodology is important for ISAC or any system, as it is essential for assessing performance and guiding future upgrades. Such a methodology does not exist for ISAC. This Thesis tackles these challenges by emphasizing the importance of considering the main features to construct an ISAC channel: Correlation between the sensing and communication channel and spatial consistency. Building on the initial development of the ISAC framework, the next step involves testing ISAC in quasi-realistic environments. This Thesis presents an industrial use case that applies sensing-assisted beam training, demonstrating how ISAC can deal with the issue of multiple obstructions in such environments. Specifically, it explores the background subtraction technique in a predictive beamforming algorithm, which leverages target-related information obtained through sensing. Under these considerations, the findings indicate a substantial improvement in communication performance, particularly regarding signal-to-noise ratio (SNR) and effective data rate. In other words, the results highlight ISAC's potential to tackle the geometrical complexities of the environment of interest effectively. This Thesis not only pioneers the background subtraction technique but also showcases its impact, paving the way for future applications to other sensing algorithms within the framework of ISAC and the factory of the future. / Thanks to the Spanish Ministry of Science, Innovation, and University, which funded this Thesis under Project Grant No. RTI2018-099880-B-C31 / Ramos Pillasagua, AF. (2024). 6G Integrated Sensing and Communication System for the Factory of the Future [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/214344