Modern analytical instrumentation is capable of creating enormous and complex volumes of data. Analysis of large data volumes are complicated by lengthy analysis time and high computational demand. Incorporating real-time analysis methods that are computationally efficient are desirable for modern analytical methods to be fully utilized. The use of modern instrumentation in on-line pharmaceutical process validation, remote sensing, and astrobiology applications requires real-time analysis methods that are computationally efficient. Integrated sensing and processing (ISP) is a method for minimizing the data burden and sensing time of a system. ISP is accomplished through implementation of chemometric calculations in the physics of the spectroscopic sensor itself. In ISP, the measurements collected at the detector are weighted to directly correlate to the sample properties of interest. This method is especially useful for large and complex data sets. In this research, ISP is applied to acoustic resonance spectroscopy, near-infrared hyperspectral imaging and a novel solid state spectral imager. In each application ISP produced a clear advantage over the traditional sensing method. The limitations of ISP must be addressed before it can become widely used. ISP is essentially a pattern recognition algorithm. Problems arise in pattern recognition when the pattern-recognition algorithm encounters a sample unlike any in the original calibration set. This is termed the false sample problem. To address the false sample problem the Bootstrap Error-Adjusted Single-Sample Technique (BEST, a nonparametric classification technique) was investigated. The BEST-ISP method utilizes a hashtable of normalized BEST points along an asymmetric probability density contour to estimate the BEST multidimensional standard deviation of a sample. The on-line application of the BEST method requires significantly less computation than the full algorithm allowing it to be utilized in real time as sample data is obtained. This research tests the hypothesis that a BEST-ISP metric can be used to detect false samples with sensitivity > 90% and specificity > 90% on categorical data.

Many areas of science now generate huge volumes of data that present visualization, modeling, and interpretation challenges. Methods for effectively representing the original data in a reduced coordinate space are therefore receiving much attention. The purpose of this research is to test the hypothesis that molecular computing of vectors for transformation matrices enables spectra to be represented in any arbitrary coordinate system. New coordinate systems are selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a spectrometer. A novel integrated sensing and processing system, termed Molecular Factor Computing (MFC) based near infrared (NIR) spectrometer, is proposed in this dissertation. In an MFC -based NIR spectrometer, spectral features are encoded by the transmission spectrum of MFC filters which effectively compute the calibration function or the discriminant functions by weighing the signals received from a broad wavelength band. Compared with the conventional spectrometers, the novel NIR analyzer proposed in this work is orders of magnitude faster and more rugged than traditional spectroscopy instruments without sacrificing the accuracy that makes it an ideal analytical tool for process analysis. Two different MFC filter-generating algorithms are developed and tested for searching a near-infrared spectral library to select molecular filters for MFC-based spectroscopy. One using genetic algorithms coupled with predictive modeling methods to select MFC filters from a spectral library for quantitative prediction is firstly described. The second filter-generating algorithm designed to select MFC filters for qualitative classification purpose is then presented. The concept of molecular factor computing (MFC)-based predictive spectroscopy is demonstrated with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument.

The growing adoption of millimeter wave (mmWave) turns efficient beamforming and beam management procedures into key enablers for 5th Generation (5G) and Beyond 5G (B5G) mobile networks. Recent research has sought to optimize beam management in modern Radio Access Network (RAN) architectures, where open, virtualized, disaggregated and multi-vendor environments are considered, and management platforms allow the use of Artificial Intelligence (AI) and Machine Learning (ML)-based solutions. Moreover, beam management represents some fundamental use cases defined by Open RAN Alliance (O-RAN). This work analyses beam management strategies in Open RAN and proposes solutions for codebook-based mmWave systems inspired by two use cases from O-RAN: the Grid of Beams (GoB) Optimization and the AI/ML-assisted Beam Selection. For the GoB Optimization use case, a scenario subject to constraints on the use of the full GoB due to overhead during beam selection is considered. An Advantage Actor Critic (A2C) learning-based framework is proposed to optimize the GoB, as well as the transmission power in a mmWave network. The proposed technique improves Energy Efficiency (EE) and ensures fair coverage is maintained. The simulations show that A2C-based joint optimization of GoB and transmission power is more effective than using Equally Spaced Beams (ESB) and fixed power, or the optimization of GoB and transmission power disjointly. Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test, and it is closer to the maximum theoretical EE. In the case of the AI/ML-assisted Beam Selection use case, the overhead during beam selection is addressed by a multi-modal sensing-aided ML-based method. When using sensing information sources external to the RAN in a multi-vendor disaggregated environment, such methods must account for privacy and data ownership issues. A Distributed Machine Learning (DML) strategy based on Split Learning (SL) is proposed to this end. The solution can cope with deployment challenges in novel RAN architectures and is applied to single and multi-level beam selection decisions, where the latter considers hierarchical codebook structures. With the proposed approach, accuracy levels above 90% can be achieved, while overhead decreases by 85% or more. SL achieves performance comparable to the centralized learning-based strategies, with the added value of accounting for privacy and data ownership issues.

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>

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