The emergence of 6G and beyond networks is set to enable a range of novel services such as personalized highly immersive experiences, holographic teleportation, and human-like intelligent robotic applications. Such applications require a set of stringent sensing, communication, control, and intelligence requirements that mandate a leap in the design, analysis, and optimization of today's wireless networks. First, from a wireless communication standpoint, future 6G applications necessitate extreme requirements in terms of bidirectional data rates, near-zero latency, synchronization, and jitter. Concurrently, such services also need a sensing functionality to track, localize, and sense their environment. Owing to its abundant bandwidth, one may naturally resort to terahertz (THz) frequency bands (0.1 − 10 THz) so as to provide significant wireless capacity gains and enable high-resolution environment sensing. Nonetheless, operating a wireless system at the THz band is constrained by a very uncertain channel which brings forth novel challenges. In essence, these channel limitations lead to unreliable intermittent links ergo the short communication range and the high susceptibility to blockage and molecular absorption. Second, given that emerging wireless services are "intelligence-centric", today's communication links must be transformed from a mere bit-pipe into a brain-like reasoning system. Towards this end, one can exploit the concept of semantic communications, a revolutionary paradigm that promises to transform radio nodes into intelligent agents that can extract the underlying meaning (semantics) or significance in a data stream. However, to date, there has been a lack in holistic, fundamental, and scalable frameworks for building next-generation semantic communication networks based on rigorous and well-defined technical foundations. Henceforth, to panoramically develop the fully-fledged theoretical foundations of future 6G applications and guarantee affluent corresponding experiences, this dissertation thoroughly investigates two thrusts. The first thrust focuses on developing the analytical foundations of THz systems with a focus on network design, performance analysis, and system optimization. First, a novel and holistic vision that articulates the unique role of THz in 6G systems is proposed. This vision exposes the solutions and milestones necessary to unleash THz's true potential in next-generation wireless systems. Then, given that extended reality (XR) will be a staple application of 6G systems, a novel risk and tail-based performance analysis is proposed to evaluate the instantaneous performance of THz bands for specific ultimate virtual reality (VR) services. Here, the results showcase that abundant bandwidth and the molecular absorption effect have only a secondary effect on the reliability compared to the availability of line-of-sight. More importantly, the results highlight that average metrics overlook extreme events and tend to provide false positive performance guarantees. To address the identified challenges of THz systems, a risk-oriented learning-based design that exploits reconfigurable intelligent surfaces (RISs) is proposed so as to optimize the instantaneous reliability. Furthermore, the analytical results are extended to investigate the uplink freshness of augmented reality (AR) services. Here, a novel ruin-based performance is conducted that scrutinizes the peak age of information (PAoI) during extreme events. Next, a novel joint sensing, communication, and artificial intelligence (AI) framework is developed to turn every THz communication link failure into a sensing opportunity, with application to digital world experiences with XR. This framework enables the use of the same waveform, spectrum, and hardware for both sensing and communication functionalities. Furthermore, this sensing input is intelligently processed via a novel joint imputation and forecasting system that is designed via non-autoregressive and transformed-based generative AI tools. This joint system enables fine-graining the sensing input to smaller time slots, predicting missing values, and fore- casting sensing and environmental information about future XR user behavior. Then, a novel joint quality of personal experience (QoPE)-centric and sensing-driven optimization is formulated and solved via deep hysteretic multi-agent reinforcement learning tools. Essentially, this dissertation establishes a solid foundation for the future deployment of THz frequencies in next-generation wireless networks through the proposal of a comprehensive set of principles that draw on the theories of tail and risk, joint sensing and communication designs, and novel AI frameworks. By adopting a multi-faceted approach, this work contributes significantly to the understanding and practical implementation of THz technology, paving the way for its integration into a wide range of applications that demand high reliability, resilience, and an immersive user experience. In the second thrust of this dissertation, the very first theoretical foundations of semantic communication and AI-native wireless networks are developed. In particular, a rigorous and holistic vision of an end-to-end semantic communication network that is founded on novel concepts from AI, causal reasoning, transfer learning, and minimum description length theory is proposed. Within this framework, the dissertation demonstrates that moving from data-driven intelligence towards reasoning-driven intelligence requires identifying association (statistical) and causal logic. Additionally, to evaluate the performance of semantic communication networks, novel key performance indicators metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the imminent convergence of computing and communication resources. Then, a novel contrastive learning framework is proposed so as to disentangle learnable and memoizable patterns in source data and make the data "semantic-ready". Through the development of a rigorous end-to-end semantic communication network founded on novel concepts from communication theory and AI, along with the proposal of novel performance metrics, this dissertation lays a solid foundation for the advancement of reasoning-driven intelligence in the field of wireless communication and paves the way for a wide range of future applications. Ultimately, the various analytical foundations presented in this dissertation will provide key guidelines that guarantee seamless experiences in future 6G applications, enable a successful deployment of THz wireless systems as a versatile band for integrated communication and sensing, and build future AI-native semantic communication networks. / Doctor of Philosophy / To date, the evolution of wireless networks has been driven by a chase for data rates, i.e., higher download or upload speeds. Nonetheless, future 6G applications (the generation succeeding today's fifth generation 5G), such as the metaverse, extended reality (encompassing augmented, mixed, and virtual reality), and fully autonomous robots and vehicles, necessitate a major leap in the design and functionality of a wireless network. Firstly, wireless networks must be able to perform functionalities that go beyond communications, encompassing control, sensing, and localization. Such functionalities enable a wide range of tasks such as remotely controlling a device, or tracking a mobile equipment with high precision. Secondly, wireless networks must be able to deliver experiences (e.g. provide the user a sense of immersion in a virtual world), in contrast to a mere service. To do so, extreme requirements in terms of data rate, latency, reliability, and sensing resolution must be met. Thirdly, intelligence must be native to wireless networks, which means that they must possess cognitive and reasoning abilities that enable them to think, act, and communicate like human beings. In this dissertation, the three aforementioned key enablers of future 6G experiences are examined. Essentially, one of the focuses of this dissertation is the design, analysis, and optimization of wireless networks operating at the so-called terahertz (THz) frequency band. The THz band is a quasi-optical (close to the visible light spectrum) frequency band that can enable wireless networks to potentially provide the extreme speeds needed (in terms of communications) and the high-resolution sensing. However, such frequency bands tend to be very susceptible to obstacles, humidity, and many other weather conditions. Therefore, this dissertation investigates the potential of such bands in meeting the demands of future 6G applications. Furthermore, novel solutions, enablers, and optimization frameworks are investigated to facilitate the successful deployment of this frequency band. To provide wireless networks with their reasoning ability, this dissertation comprehensively investigates the concept of semantic communications. In contrast to today's traditional communication frameworks that convert our data to binary bits (ones and zeros), semantic communication's goal is to enable networks to communicate meaning (semantics). To successfully engineer and deploy such networks, this dissertation proposes a novel suite of communication theoretic tools and key performance indicators. Subsequently, this dissertation proposes and analyzes a set of novel artificial intelligence (AI) tools that enable wireless networks to be equipped with the aforementioned cognitive and reasoning abilities. The outcomes of this dissertation have the potential to transform the way we interact with technology by catalyzing the deployment of holographic societies, revolutionizing the healthcare via remote augmented surgery, and facilitating the deployment of autonomous vehicles for a safer and more efficient transportation system. Additionally, the advancements in wireless networks and artificial intelligence proposed in this dissertation could also have a significant impact on various other industries, such as manufacturing, education, and defense, by enabling more efficient and intelligent systems. Ultimately, the societal impact of this research is far-reaching and could contribute to creating a more connected and advanced world.

Submitted by Maria Suzana Diniz (msuzanad@hotmail.com) on 2015-11-05T14:36:51Z No. of bitstreams: 1 arquivototal.pdf: 4812802 bytes, checksum: e5276e62e6294058de196c6252bedc95 (MD5) / Made available in DSpace on 2015-11-05T14:36:51Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 4812802 bytes, checksum: e5276e62e6294058de196c6252bedc95 (MD5) Previous issue date: 2015-02-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work deals with the evaluation of the communication system by radio frequency using the NRF-24L01+® transceiver to be used in monitoring of electrical systems on the concept of smart grid. The complete system consists of one or more Units Remote Data Acquisition - URDAs; multiple Smart Sensing Units - SSUs; and Supervisory Control Subsystem - SCS. The connection between URAD and SSUs may occur via wired connection (Ethernet, RS232, USB, CAN or PLC) and wireless (RF). URADs fit to the acquisition, processing and communication of variables with low time constant while the USIs are primarily responsible for the acquisition of magnitudes with larger time constants (temperature, pressure, humidity, etc.). In this work, we focus on development and communication of SSUs. For these tests the units are of two types: a master unit, responsible for requesting data (wireless) and sending the SCS (Communication RS232, USB, CAN or RF); and a slave unit, which may account for the measured variables of interest to send to the master unit when requested. For wireless communication (RF), the transceiver nRF - 24L01+® from NORDICTM was used, because its processing characteristics and communication satisfactorily meet the needs and requirements of the project, which will be addressed in the course of this work. / O presente trabalho trata da avaliação do sistema de comunicação por meio de rádio-frequência utilizando o transceiver nRF-24L01+® para ser utilizado no monitoramento de sistemas elétricos no conceito de smart grid. O sistema completo é composto de uma ou mais Unidades Remotas de Aquisição de Dados – URADs; de várias Unidades de Sensoriamento Inteligente – USIs; e um Subsistema de Controle Supervisório – SCS. A conexão entre a URAD e as USIs pode ocorrer através de conexão cabeada (Ethernet, RS232, USB, CAN ou PLC) e sem fio (RF). Cabem às URADs a aquisição, processamento e comunicação das grandezas com pequena constante de tempo, enquanto que as USIs encarregam-se da aquisição de grandezas com constantes de tempo maiores (temperatura, pressão, umidade, etc.). Neste trabalho, tratamos do desenvolvimento e de testes de comunicação da USI. Para estes testes as unidades são de 2 tipos: uma unidade mestre, responsável pela requisição dos dados (sem fio) e pelo envio ao SCS (comunicação RS232, USB, CAN ou RF); e uma unidade escravo, que pode ser responsável pela medição de grandezas de interesse para envio à unidade mestre quando requisitada. Para a comunicação sem fio (RF), utilizou-se o transceptor nRF-24L01+® da NORDICTM, pois suas características de processamento e comunicação atendem satisfatoriamente às necessidades e exigências do projeto, que serão abordadas no transcurso deste trabalho.

<p dir="ltr">Sensing and communication circuits and systems are crucial components in various electronic devices and technologies. These systems are designed to acquire information from the surrounding environment through sensors, process that information, and facilitate communication between different devices or systems. It plays a vital role in modern electronic devices, enabling them to collect, process, and exchange information to perform various functions in applications such as IoB (Internet of Body), healthcare, hardware security, industrial automation, and more.</p><p dir="ltr">This work focuses on innovations in sensing and communication circuits spanning two independent application areas – human body communication and hardware emanations security.</p><p dir="ltr">First, an ultra-low power ECG monitoring system is implemented to perpetually power itself using Thermoelectric Generator (TEG) to harvest body energy while securely transmitting sensed data through on-body communication, achieving closed-loop operation without external charging or batteries. Custom circuits allow demonstrated feasibility of self-sustaining wearables leveraging Human Body Communication’s advantages.</p><p dir="ltr">Second, investigations reveal vulnerabilities introduced when repairing broken cables, with unintended monopole antennas boosting electromagnetic emissions containing signal correlations. Experiments characterize long-range detection regimes post-repair across USB keyboard cables. Further circuit and structural innovations provide localized shielding at repair points as a potential mitigation. Advancements offer contributions in understanding hardware emission security risks to inform protection strategies.</p><p dir="ltr">The two separate research work demonstrate specialized circuits advancing the state-of-the-art in sensing and communication for wearable body-based systems and hardware security through greater awareness of vulnerabilities from unintended emissions.</p><p><br></p>

Future mobile networks are anticipated to not only enhance communication performance but also facilitate new sensing-based applications. This highlights the essential role of integrated sensing and communication (ISAC) in sixth-generation (6G) and beyond mobile networks. The seamless integration of sensing and communication poses challenges in deployment and resource allocation. Cell-free massive multiple-input multiple-output (MIMO) networks, characterized by multiple distributed access points, offer a promising infrastructure for ISAC implementation. However, the effective realization of ISAC necessitates joint design and resource allocation optimization. In this thesis, we study ISAC within cell-free massive MIMO systems, with a particular emphasis on developing power allocation algorithms under various scenarios. In this thesis, we explore two scenarios: utilizing existing communication signals and incorporating additional sensing signals. We propose power allocation algorithms aiming to maximize the sensing performance while meeting communication and power constraints. In addition, we develop two maximum a posteriori ratio test (MAPRT) target detectors under clutter-free and cluttered scenarios. Results indicate that employing additional sensing signals enhances sensing performance, particularly in scenarios where the target has low reflectivity. Moreover, although the clutter-aware detector requires more advanced processing, it leads to better sensing performance. Furthermore, we introduced sensing spectral efficiency (SE) to measure the effect of resource block utilization, highlighting the integration advantages of ISAC over orthogonal resource sharing approaches.  In the next part of the thesis, we study the energy efficiency aspects of ISAC in cell-free massive MIMO systems with ultra-reliable low-latency communications (URLLC) users. We propose a power allocation algorithm aiming to maximize energy efficiency of the system while meeting communication and sensing requirements. We conduct a comparative analysis between the proposed power allocation algorithms and a URLLC-only approach which takes into account only URLLC and power requirements. The results reveal that while the URLLC-only algorithm excels in energy efficiency, it is not able to support sensing requirements.   Moreover, we study the impact of ISAC on end-to-end (including radio and processing) energy consumption. Particularly, we present giga-operations per second (GOPS) analysis for both communication and sensing tasks. Two optimization problems are formulated and solved to minimize transmission and end-to-end energy through blocklength and power optimization. Results indicate that while end-to-end energy minimization offers substantial energy savings, its efficacy diminishes with sensing integration due to processing energy requirements. / Framtida mobila nätverk förväntas inte bara förbättra kommunikations-prestanda utan även mögliggöra nya applikationer baserade på sensorer. Dettaunderstryker den avgörande rollen för Integrerad avkänning och kommunika-tion (ISAC) i sjätte generationens (6G) och efterföljande mobila nätverk. Densömlösa integrationen av sensorer och kommunikation medför utmaningar iutrullning och resursallokering. Cellfria massiva flerantennsystem (MIMO-nätverk), kännetecknade av flera distribuerade åtkomstpunkter, erbjuder enlovande infrastruktur för implementering av ISAC. Dock kräver den effektivarealiseringen av ISAC samverkande design och optimering av resursallokering.I denna avhandling studerar vi ISAC inom cellfria massiva MIMO-system,med särskild tonvikt på att utveckla effektallokeringsalgoritmer under olikascenarier.Vi utforskar två scenarier: att utnyttja befintliga kommunikationssignaleroch att inkludera ytterligare sensorssignaler. Vi föreslår effektallokeringsalgo-ritmer med målet att maximera sensorsprestandan samtidigt som kommunika-tions och effektbegränsningar uppfylls. Dessutom utvecklar vi två detektorerbaserade på maximum a posteriori ratio test (MAPRT) under störningsfriaoch störda scenarier. Resultaten visar att användning av ytterligare sensors-signaler förbättrar sensorsprestandan, särskilt i scenarier där målet har lågreflektivitet. Dessutom, även om den störkänsliga detektorn kräver mer avan-cerad bearbetning, leder den till bättre sensorsprestanda. Vidare introducerarvi sensorerspektral effektivitet (SE) för att mäta effekten av resursblocksan-vändning och framhäva integrationsfördelarna med ISAC över ortogonala re-sursdelningsmetoder.I den andra delen av avhandlingen studerar vi energieffektivitetsaspek-terna av ISAC i cellfria massiva MIMO-system med användare med ultra-tillförlitlig låg-latens (URLLC) kommunikation. Vi föreslår en effektalloke-ringsalgoritm med syfte att maximera systemets energieffektivitet samtidigtsom kommunikations- och sensorskraven uppfylls. Vi utför en jämförande ana-lys mellan de föreslagna effektallokeringsalgoritmerna och ett URLLC-ensamttillvägagångssätt som tar hänsyn enbart till URLLC- och effektkrav. Resul-taten avslöjar att medan URLLC-ensamma algoritmen utmärker sig i energi-effektivitet, kan den inte stödja sensorskraven. Dessutom studerar vi effektenav ISAC på slut till slut (inklusive radios och bearbetning) energiförbruk-ning. Särskilt presenterar vi giga-operationer per sekund (GOPS) analys förbåde kommunikations- och sensorsuppgifter. Två optimeringsproblem formu-leras och löses för att minimera överförings- och slut till slut energi genomblocklängd- och effektoptimering. Resultaten indikerar att medan slut till slutenergiminimering erbjuder betydande energibesparingar, minskar dess effek-tivitet med sensorintegrationen på grund av bearbetningsenergikrav. / <p>QC 20240513</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