Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing

Unsere moderne Gesellschaft ist Zeuge eines fundamentalen Wandels in der Art und Weise wie wir mit Technologie interagieren. Geräte werden zunehmend intelligenter - sie verfügen über mehr und mehr Rechenleistung und häufiger über eigene Kommunikationsschnittstellen. Das beginnt bei einfachen Haushaltsgeräten und reicht über Transportmittel bis zu großen überregionalen Systemen wie etwa dem Stromnetz. Die Erfassung, die Verarbeitung und der Austausch digitaler Informationen gewinnt daher immer mehr an Bedeutung. Die Tatsache, dass ein wachsender Anteil der Geräte heutzutage mobil und deshalb batteriebetrieben ist, begründet den Anspruch, digitale Signalverarbeitungsalgorithmen besonders effizient zu gestalten. Dies kommt auch dem Wunsch nach einer Echtzeitverarbeitung der großen anfallenden Datenmengen zugute. Die vorliegende Arbeit demonstriert Methoden zum Finden effizienter algebraischer Lösungen für eine Vielzahl von Anwendungen mehrkanaliger digitaler Signalverarbeitung. Solche Ansätze liefern nicht immer unbedingt die bestmögliche Lösung, kommen dieser jedoch häufig recht nahe und sind gleichzeitig bedeutend einfacher zu beschreiben und umzusetzen. Die einfache Beschreibungsform ermöglicht eine tiefgehende Analyse ihrer Leistungsfähigkeit, was für den Entwurf eines robusten und zuverlässigen Systems unabdingbar ist. Die Tatsache, dass sie nur gebräuchliche algebraische Hilfsmittel benötigen, erlaubt ihre direkte und zügige Umsetzung und den Test unter realen Bedingungen. Diese Grundidee wird anhand von drei verschiedenen Anwendungsgebieten demonstriert. Zunächst wird ein semi-algebraisches Framework zur Berechnung der kanonisch polyadischen (CP) Zerlegung mehrdimensionaler Signale vorgestellt. Dabei handelt es sich um ein sehr grundlegendes Werkzeug der multilinearen Algebra mit einem breiten Anwendungsspektrum von Mobilkommunikation über Chemie bis zur Bildverarbeitung. Verglichen mit existierenden iterativen Lösungsverfahren bietet das neue Framework die Möglichkeit, den Rechenaufwand und damit die Güte der erzielten Lösung zu steuern. Es ist außerdem weniger anfällig gegen eine schlechte Konditionierung der Ausgangsdaten. Das zweite Gebiet, das in der Arbeit besprochen wird, ist die unterraumbasierte hochauflösende Parameterschätzung für mehrdimensionale Signale, mit Anwendungsgebieten im RADAR, der Modellierung von Wellenausbreitung, oder bildgebenden Verfahren in der Medizin. Es wird gezeigt, dass sich derartige mehrdimensionale Signale mit Tensoren darstellen lassen. Dies erlaubt eine natürlichere Beschreibung und eine bessere Ausnutzung ihrer Struktur als das mit Matrizen möglich ist. Basierend auf dieser Idee entwickeln wir eine tensor-basierte Schätzung des Signalraums, welche genutzt werden kann um beliebige existierende Matrix-basierte Verfahren zu verbessern. Dies wird im Anschluss exemplarisch am Beispiel der ESPRIT-artigen Verfahren gezeigt, für die verbesserte Versionen vorgeschlagen werden, die die mehrdimensionale Struktur der Daten (Tensor-ESPRIT), nichzirkuläre Quellsymbole (NC ESPRIT), sowie beides gleichzeitig (NC Tensor-ESPRIT) ausnutzen. Um die endgültige Schätzgenauigkeit objektiv einschätzen zu können wird dann ein Framework für die analytische Beschreibung der Leistungsfähigkeit beliebiger ESPRIT-artiger Algorithmen diskutiert. Verglichen mit existierenden analytischen Ausdrücken ist unser Ansatz allgemeiner, da keine Annahmen über die statistische Verteilung von Nutzsignal und Rauschen benötigt werden und die Anzahl der zur Verfügung stehenden Schnappschüsse beliebig klein sein kann. Dies führt auf vereinfachte Ausdrücke für den mittleren quadratischen Schätzfehler, die Schlussfolgerungen über die Effizienz der Verfahren unter verschiedenen Bedingungen zulassen. Das dritte Anwendungsgebiet ist der bidirektionale Datenaustausch mit Hilfe von Relay-Stationen. Insbesondere liegt hier der Fokus auf Zwei-Wege-Relaying mit Hilfe von Amplify-and-Forward-Relays mit mehreren Antennen, da dieser Ansatz ein besonders gutes Kosten-Nutzen-Verhältnis verspricht. Es wird gezeigt, dass sich die nötige Kanalkenntnis mit einem einfachen algebraischen Tensor-basierten Schätzverfahren gewinnen lässt. Außerdem werden Verfahren zum Finden einer günstigen Relay-Verstärkungs-Strategie diskutiert. Bestehende Ansätze basieren entweder auf komplexen numerischen Optimierungsverfahren oder auf Ad-Hoc-Ansätzen die keine zufriedenstellende Bitfehlerrate oder Summenrate liefern. Deshalb schlagen wir algebraische Ansätze zum Finden der Relayverstärkungsmatrix vor, die von relevanten Systemmetriken inspiriert sind und doch einfach zu berechnen sind. Wir zeigen das algebraische ANOMAX-Verfahren zum Erreichen einer niedrigen Bitfehlerrate und seine Modifikation RR-ANOMAX zum Erreichen einer hohen Summenrate. Für den Spezialfall, in dem die Endgeräte nur eine Antenne verwenden, leiten wir eine semi-algebraische Lösung zum Finden der Summenraten-optimalen Strategie (RAGES) her. Anhand von numerischen Simulationen wird die Leistungsfähigkeit dieser Verfahren bezüglich Bitfehlerrate und erreichbarer Datenrate bewertet und ihre Effektivität gezeigt.Modern society is undergoing a fundamental change in the way we interact with technology. More and more devices are becoming "smart" by gaining advanced computation capabilities and communication interfaces, from household appliances over transportation systems to large-scale networks like the power grid. Recording, processing, and exchanging digital information is thus becoming increasingly important. As a growing share of devices is nowadays mobile and hence battery-powered, a particular interest in efficient digital signal processing techniques emerges. This thesis contributes to this goal by demonstrating methods for finding efficient algebraic solutions to various applications of multi-channel digital signal processing. These may not always result in the best possible system performance. However, they often come close while being significantly simpler to describe and to implement. The simpler description facilitates a thorough analysis of their performance which is crucial to design robust and reliable systems. The fact that they rely on standard algebraic methods only allows their rapid implementation and test under real-world conditions. We demonstrate this concept in three different application areas. First, we present a semi-algebraic framework to compute the Canonical Polyadic (CP) decompositions of multidimensional signals, a very fundamental tool in multilinear algebra with applications ranging from chemistry over communications to image compression. Compared to state-of-the art iterative solutions, our framework offers a flexible control of the complexity-accuracy trade-off and is less sensitive to badly conditioned data. The second application area is multidimensional subspace-based high-resolution parameter estimation with applications in RADAR, wave propagation modeling, or biomedical imaging. We demonstrate that multidimensional signals can be represented by tensors, providing a convenient description and allowing to exploit the multidimensional structure in a better way than using matrices only. Based on this idea, we introduce the tensor-based subspace estimate which can be applied to enhance existing matrix-based parameter estimation schemes significantly. We demonstrate the enhancements by choosing the family of ESPRIT-type algorithms as an example and introducing enhanced versions that exploit the multidimensional structure (Tensor-ESPRIT), non-circular source amplitudes (NC ESPRIT), and both jointly (NC Tensor-ESPRIT). To objectively judge the resulting estimation accuracy, we derive a framework for the analytical performance assessment of arbitrary ESPRIT-type algorithms by virtue of an asymptotical first order perturbation expansion. Our results are more general than existing analytical results since we do not need any assumptions about the distribution of the desired signal and the noise and we do not require the number of samples to be large. At the end, we obtain simplified expressions for the mean square estimation error that provide insights into efficiency of the methods under various conditions. The third application area is bidirectional relay-assisted communications. Due to its particularly low complexity and its efficient use of the radio resources we choose two-way relaying with a MIMO amplify and forward relay. We demonstrate that the required channel knowledge can be obtained by a simple algebraic tensor-based channel estimation scheme. We also discuss the design of the relay amplification matrix in such a setting. Existing approaches are either based on complicated numerical optimization procedures or on ad-hoc solutions that to not perform well in terms of the bit error rate or the sum-rate. Therefore, we propose algebraic solutions that are inspired by these performance metrics and therefore perform well while being easy to compute. For the MIMO case, we introduce the algebraic norm maximizing (ANOMAX) scheme, which achieves a very low bit error rate, and its extension Rank-Restored ANOMAX (RR-ANOMAX) that achieves a sum-rate close to an upper bound. Moreover, for the special case of single antenna terminals we derive the semi-algebraic RAGES scheme which finds the sum-rate optimal relay amplification matrix based on generalized eigenvectors. Numerical simulations evaluate the resulting system performance in terms of bit error rate and system sum rate which demonstrates the effectiveness of the proposed algebraic solutions

Direction of Arrival Estimation and Tracking with Sparse Arrays

Direction of Arrival (DOA) estimation and tracking of a plane wave or multiple plane waves impinging on an array of sensors from noisy data are two of the most important tasks in array signal processing, which have attracted tremendous research interest over the past several decades. It is well-known that the estimation accuracy, angular resolution, tracking capacity, computational complexity, and hardware implementation cost of a DOA estimation and/or tracking technique depend largely on the array geometry. Large arrays with many sensors provide accurate DOA estimation and perfect target tracking, but they usually suffer from a high cost for hardware implementation. Sparse arrays can yield similar DOA estimates and tracking performance with fewer elements for the same-size array aperture as compared to the traditional uniform arrays. In addition, the signals of interest may have rich temporal information that can be exploited to effectively eliminate background noise and significantly improve the performance and capacity of DOA estimation and tracking, and/or even dramatically reduce the computational burden of estimation and tracking algorithms. Therefore, this thesis aims to provide some solutions to improving the DOA estimation and tracking performance by designing sparse arrays and exploiting prior knowledge of the incident signals such as AR modeled sources and known waveforms. First, we design two sparse linear arrays to efficiently extend the array aperture and improve the DOA estimation performance. One scheme is called minimum redundancy sparse subarrays (MRSSA), where the subarrays are used to obtain an extended correlation matrix according to the principle of minimum redundancy linear array (MRLA). The other linear array is constructed using two sparse ULAs, where the inter-sensor spacing within the same ULA is much larger than half wavelength. Moreover, we propose a 2-D DOA estimation method based on sparse L-shaped arrays, where the signal subspace is selected from the noise-free correlation matrix without requiring the eigen-decomposition to estimate the elevation angle, while the azimuth angles are estimated based on the modified total least squares (TLS) technique. Second, we develop two DOA estimation and tracking methods for autoregressive (AR) modeled signal source using sparse linear arrays together with Kalman filter and LS-based techniques. The proposed methods consist of two common stages: in the first stage, the sources modeled by AR processes are estimated by the celebrated Kalman filter and in the second stage, the efficient LS or TLS techniques are employed to estimate the DOAs and AR coefficients simultaneously. The AR-modeled sources can provide useful temporal information to handle cases such as the ones, where the number of sources is larger than the number of antennas. In the first method, we exploit the symmetric array to transfer a complex-valued nonlinear problem to a real-valued linear one, which can reduce the computational complexity, while in the second method, we use the ordinary sparse arrays to provide a more accurate DOA estimation. Finally, we study the problem of estimating and tracking the direction of arrivals (DOAs) of multiple moving targets with known signal source waveforms and unknown gains in the presence of Gaussian noise using a sparse sensor array. The core idea is to consider the output of each sensor as a linear regression model, each of whose coefficients contains a pair of DOAs and gain information corresponding to one target. These coefficients are determined by solving a linear least squares problem and then updating recursively, based on a block QR decomposition recursive least squares (QRD-RLS) technique or a block regularized LS technique. It is shown that the coefficients from different sensors have the same amplitude, but variable phase information for the same signal. Then, simple algebraic manipulations and the well-known generalized least squares (GLS) are used to obtain an asymptotically-optimal DOA estimate without requiring a search over a large region of the parameter space

Space time adaptive processing in multichannel passive radar

Nowdays, passive bistatic radar (PBR) systems have become a subject of intensive research, owing essentially to its unique features, such as low probability of interception, small size and low cost. Passive radar is a concept where illuminators of opportunity are used. In a bistatic passive radar the main challenges are: estimating the reference signal which is required for detection, mitigating the direct signal, multipath and clutter echoes on the surveillance channel and finally achieving a sufficient SINR to detect targets. This thesis is concerned with the definition and application of adaptive signal processing techniques to a multichannel passive radar receiver. Adaptive signal processing techniques are well known for active pulse radars. A PBR system operates in a continuous mode, therefore the received signal is not avalaible in the classical array elements-slow time-range domain such as in active pulse radar. A major component of this research focuses on demonstrating the applicability of traditional adaptive algorithms, developed in the active radar contest, with passive radar. Firstly a new detailed formulation of the sub optimum “batches algorithm”, used to evaluate the cross correlation function, is proposed. Then innovative 1D temporal adaptive processing techniques are defined extending the matched filter concept to an adaptive matched filter formulation. Afterwards a new spatial adaptive technique, based on the application of the adaptive digital beamforming after the matched filter, is investigated. Finally both 1D spatial and temporal adaptive techniques are extended to 2D space-time adaptive processing techniques. Specifically we demonstrate the applicability of STAP processing to a passive bistatic radar and we show how the classical STAP algorithms, developed for active radar systems, can be applied to a PBR system. The new defined passive radar signal processing architectures are compared with the standard approaches and the effectiveness of the proposed techniques is demonstrated considering both simulated and real data

Multifunction Transceiver Architecture and Technology for Future Wireless Systems

RÉSUMÉ Depuis la toute première transmission sans fil, les ondes radiofréquences ont été progressivement mises en valeur et exploitées dans un nombre de plus en plus important d'applications. Parmi toutes ces applications, la détection et la télécommunication sont sans doute les plus indispensables de nos jours. Il existe un grand nombre d’utilisations des radiofréquences, incluant les transports intelligents pour lesquels les véhicules doivent être équipés à la fois de radars et de dispositifs de communication afin d’être capables de détecter l'environnement ainsi que de réaliser la communication avec d'autres unités embarquées. La technologie émergente 5G est un autre exemple pour lequel plusieurs capteurs et radios devraient être capables de coopérer de manière autonome ou semi-autonome. Les principes de fonctionnement des systèmes radars et radio sont toutefois différents. Ces différences fondamentales peuvent entraîner l'utilisation de différentes architectures de traitement du signal et d'émetteur-récepteur, ce qui peut poser des problèmes pour l'intégration de toutes les fonctions requises au sein d'une seule et même plate-forme. En dehors de cela, certaines applications requièrent plusieurs fonctions simultanément dans un même dispositif. Par exemple, les systèmes de détection d'angle d'arrivée 2D nécessitent d'estimer l'angle d'arrivée (AOA) du faisceau entrant dans les plans horizontal et vertical simultanément. La communication radio multi-bandes et multi-modes est un autre exemple pour lequel un système radio doit être capable de communiquer dans plusieurs bandes de fréquences et dans plusieurs modes, par exemple, un duplexage en fonction de la fréquence ou du temps. À première vue, on peut penser que l'assemblage de plusieurs dispositifs distincts n'est pas la meilleure solution en ce qui concerne le coût, la simplicité et la fonctionnalité. Par conséquent, une direction de recherche consiste à proposer une architecture d'émetteur-récepteur unifiée et compacte plutôt qu’une plate-forme assemblant de multiples dispositifs distincts. C’est cette problématique qui est spécifiquement abordée dans ce travail. Selon les fonctions à intégrer dans un seul et unique système multifonctionnel, la solution peut traiter plusieurs aspects simultanément. Par exemple, toute solution réalisant l'intégration de fonctions liées au radar et à la radio devrait traiter deux aspects principaux, à savoir : la forme d'onde opérationnelle et l'architecture frontale RF.----------ABSTRACT Since the very early wireless transmission of radiofrequency signals, it has been gradually flourished and exploited in a wider and wider range of applications. Among all those applications of radio technology, sensing and communicating are undoubtedly the most indispensable ones. There are a large number of practical scenarios such as intelligent transportations in which vehicles must be equipped with both radar and communication devices to be capable of both sensing the environment and communication with other onboard units. The emerging 5G technology can be another important example in which multiple sensors and radios should be capable of cooperating with each other in an autonomous or semi-autonomous manner. The operation principles of these radar and radio devices are different. Such fundamental differences can result in using different operational signal, distinct signal processing, and transceiver architectures in these systems that can raise challenges for integration of all required functions within a single platform. Other than that, there exist some applications where several functions of a single device (i.e. sensor or radio) are required to be executed simultaneously. For example, 2D angle-of-arrival detection systems require estimating the angle of arrival (AOA) of the incoming beam in both horizontal and vertical planes at the same time. Multiband and multimode radio communication is another example of this kind where a radio system is desired to be capable of communication within several frequency bands and in several modes, e.g., time or frequency division duplexing. At a first glance, one can feel that the mechanical assembling of several distinct devices is not the best solution regarding the cost, simplicity and functionality or operability. Hence, the research attempt in developing a rather unified and compact transceiver architecture as opposed to a classical platform with assembled multiple individual devices comes out of horizon, which is addressed specifically in this work. Depending on the wireless functions that are to be integrated within a single multifunction system, the solution should address multiple aspects simultaneously. For instance, any solution for integrating radar and radio related functions should be able to deal with two principal aspects, namely operational waveform and RF front-end architecture. However, in some other above- mentioned examples such as 2D DOA detection system, identical operational waveform may be used and the main challenge of functional integration would pertain to a unification of multiple mono-functional transceivers

Photonic Vector Processing Techniques for Radiofrequency Signals

[EN] The processing of radiofrequency signals using photonics means is a discipline that appeared almost at the same time as the laser and the optical fibre. Photonics offers the capability of managing broadband radiofrequency (RF) signals thanks to its low transmission attenuation, a variety of linear and non-linear phenomena and, recently, the potential to implement integrated photonic subsystems. These features open the door for the implementation of multiple functionalities including optical transportation, up and down frequency conversion, optical RF filtering, signal multiplexing, de-multiplexing, routing and switching, optical sampling, tone generation, delay control, beamforming and photonic generation of digital modulations, and even a combination of several of these functionalities. This thesis is focused on the application of vector processing in the optical domain to radiofrequency signals in two fields of application: optical beamforming, and photonic vector modulation and demodulation of digital quadrature amplitude modulations. The photonic vector control enables to adjust the amplitude and phase of the radiofrequency signals in the optical domain, which is the fundamental processing that is required in different applications such as beamforming networks for direct radiating array (DRA) antennas and multilevel quadrature modulation. The work described in this thesis include different techniques for implementing a photonic version of beamforming networks for direct radiating arrays (DRA) known as optical beamforming networks (OBFN), with the objectives of providing a precise control in terrestrial applications of broadband signals at very high frequencies above 40 GHz in communication antennas, optimizing the size and mass when compared with the electrical counterparts in space application, and presenting new photonic-based OBFN functionalities. Thus, two families of OBFNs are studied: fibre-based true time delay architectures and integrated networks. The first allow the control of broadband signals using dispersive optical fibres with wavelength division multiplexing techniques and advanced functionalities such as direction of arrival estimation in receiving architectures. In the second, passive OBFNs based on monolithically-integrated Optical Butler Matrices are studied, including an ultra-compact solution using optical heterodyne techniques in silicon-on-insulator (SOI) material, and an alternative implementing a homodyne counterpart in germanium doped silica material. In this thesis, the application of photonic vector processing to the generation of quadrature digital modulations has also been investigated. Multilevel modulations are based on encoding digital information in discrete states of phase and amplitude of an electrical signal to enhance spectral efficiency, as for instance, in quadrature modulation. The signal process required for generating and demodulating this kind of signals involves vector processing (phase and amplitude control) and frequency conversion. Unlike the common electronic or digital implementation, in this thesis, different photonic based signal processing techniques are studied to produce digital modulation (photonic vector modulation, PVM) and demodulation (PVdM). These techniques are of particular interest in the case of broadband signals where the data rate required to be managed is in the order of gigabit per second, for applications like wireless backhauling of metro optical networks (known as fibre-to-the-air). The techniques described use optical dispersion in optical fibres, wavelength division multiplexing and photonic up/down conversion. Additionally, an optical heterodyne solution implemented monolithically in a photonic integrated circuit (PIC) is also described.[ES] El procesamiento de señales de radiofrecuencia (RF) utilizando medios fotónicos es una disciplina que apareció casi al mismo tiempo que el láser y la fibra óptica. La fotónica ofrece la capacidad de manipular señales de radiofrecuencia de banda ancha, una baja atenuación, procesados basados en una amplia variedad de fenómenos lineales y no lineales y, recientemente, el potencial para implementar subsistemas fotónicos integrados. Estas características ofrecen un gran potencial para la implementación de múltiples funcionalidades incluyendo transporte óptico, conversión de frecuencia, filtrado óptico de RF, multiplexación y demultiplexación de señales, encaminamiento y conmutación, muestreo óptico, generación de tonos, líneas de retardo, conformación de haz en agrupaciones de antenas o generación fotónica de modulaciones digitales, e incluso una combinación de varias de estas funcionalidades. Esta tesis se centra en la aplicación del procesamiento vectorial en el dominio óptico de señales de radiofrecuencia en dos campos de aplicación: la conformación óptica de haces y la modulación y demodulación vectorial fotónica de señales digitales en cuadratura. El control fotónico vectorial permite manipular la amplitud y fase de las señales de radiofrecuencia en el dominio óptico, que es el procesamiento fundamental que se requiere en diferentes aplicaciones tales como las redes de conformación de haces para agrupaciones de antenas y en la modulación en cuadratura. El trabajo descrito en esta tesis incluye diferentes técnicas para implementar una versión fotónica de las redes de conformación de haces de en agrupaciones de antenas, conocidas como redes ópticas de conformación de haces (OBFN). Se estudian dos familias de redes: arquitecturas de retardo en fibra óptica y arquitecturas integradas. Las primeras permiten el control de señales de banda ancha utilizando fibras ópticas dispersivas con técnicas de multiplexado por división de longitud de onda y funcionalidades avanzadas tales como la estimación del ángulo de llegada de la señal en la antena receptora. En la segunda, se estudian redes de conformación pasivas basadas en Matrices de Butler ópticas integradas, incluyendo una solución ultra-compacta utilizando técnicas ópticas heterodinas en silicio sobre aislante (SOI), y una alternativa homodina en sílice dopado con germanio. En esta tesis, también se han investigado técnicas de procesado vectorial fotónico para la generación de modulaciones digitales en cuadratura. Las modulaciones multinivel codifican la información digital en estados discretos de fase y amplitud de una señal eléctrica para aumentar su eficiencia espectral, como por ejemplo la modulación en cuadratura. El procesado necesario para generar y demodular este tipo de señales implica el procesamiento vectorial (control de amplitud y fase) y la conversión de frecuencia. A diferencia de la implementación electrónica o digital convencional, en esta tesis se estudian diferentes técnicas de procesado fotónico tanto para la generación de modulaciones digitales (modulación vectorial fotónica, PVM) como para su demodulación (PVdM). Esto es de particular interés en el caso de señales de banda ancha, donde la velocidad de datos requerida es del orden de gigabits por segundo, para aplicaciones como backhaul inalámbrico de redes ópticas metropolitanas (conocida como fibra hasta el aire). Las técnicas descritas se basan en explotar la dispersión cromática de la fibra óptica, la multiplexación por división de longitud de onda y la conversión en frecuencia. Además, se presenta una solución heterodina implementada monolíticamente en un circuito integrado fotónico (PIC).[CA] El processament de senyals de radiofreqüència (RF) utilitzant mitjans fotònics és una disciplina que va aparèixer gairebé al mateix temps que el làser i la fibra òptica. La fotònica ofereix la capacitat de manipular senyals de radiofreqüència de banda ampla, una baixa atenuació, processats basats en una àmplia varietat de fenòmens lineals i no lineals i, recentment, el potencial per implementar subsistemes fotònics integrats. Aquestes característiques ofereixen un gran potencial per a la implementació de múltiples funcionalitats incloent transport òptic, conversió de freqüència, filtrat òptic de RF, multiplexació i demultiplexació de senyals, encaminament i commutació, mostreig òptic, generació de tons, línies de retard, conformació de feix en agrupacions d'antenes i la generació fotònica de modulacions digitals, i fins i tot una combinació de diverses d'aquestes funcionalitats. Aquesta tesi es centra en l'aplicació del processament vectorial en el domini òptic de senyals de radiofreqüència en dos camps d'aplicació: la conformació òptica de feixos i la modulació i demodulació vectorial fotònica de senyals digitals en quadratura. El control fotònic vectorial permet manipular l'amplitud i la fase dels senyals de radiofreqüència en el domini òptic, que és el processament fonamental que es requereix en diferents aplicacions com ara les xarxes de conformació de feixos per agrupacions d'antenes i en modulació multinivell. El treball descrit en aquesta tesi inclou diferents tècniques per implementar una versió fotònica de les xarxes de conformació de feixos en agrupacions d'antenes, conegudes com a xarxes òptiques de conformació de feixos (OBFN), amb els objectius de proporcionar un control precís en aplicacions terrestres de senyals de banda ampla a freqüències molt altes per sobre de 40 GHz en antenes de comunicacions, optimitzant la mida i el pes quan es compara amb els homòlegs elèctrics en aplicacions espacials, i la presentació de noves funcionalitats fotòniques per agrupacions d'antenes. Per tant, s'estudien dues famílies de OBFNs: arquitectures de retard en fibra òptica i arquitectures integrades. Les primeres permeten el control de senyals de banda ampla utilitzant fibres òptiques dispersives amb tècniques de multiplexació per divisió en longitud d'ona i funcionalitats avançades com ara l'estimació de l'angle d'arribada del senyal a l'antena receptora. A la segona, s'estudien xarxes de conformació passives basades en Matrius de Butler òptiques en fotònica integrada, incloent una solució ultra-compacta utilitzant tècniques òptiques heterodinas en silici sobre aïllant (SOI), i una alternativa homodina en sílice dopat amb germani. D'altra banda, també s'ha investigat en aquesta tesi tècniques de processament vectorial fotònic per a la generació de modulacions digitals en quadratura. Les modulacions multinivell codifiquen la informació digital en estats discrets de fase i amplitud d'un senyal elèctric per augmentar la seva eficiència espectral, com ara la modulació en quadratura. El processat necessari per generar i desmodular aquest tipus de senyals implica el processament vectorial (control d'amplitud i fase) i la conversió de freqüència. A diferència de la implementació electrònica o digital convencional, en aquesta tesi s'estudien diferents tècniques de processament fotònic tant per a la generació de modulacions digitals (modulació vectorial fotònica, PVM) com per la seva demodulació (PVdM). Això és de particular interès en el cas de senyals de banda ampla, on la velocitat de dades requerida és de l'ordre de gigabits per segon, per a aplicacions com backhaul sense fils de xarxes òptiques metropolitanes (coneguda com fibra fins l'aire). Les tècniques descrites es basen en explotar la dispersió cromàtica de la fibra òptica, la multiplexació per divisió en longitud d'ona i la conversió en freqüència. A més, es presePiqueras Ruipérez, MÁ. (2016). Photonic Vector Processing Techniques for Radiofrequency Signals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/63264TESI

Ultra-Wideband Secure Communications and Direct RF Sampling Transceivers

Larger wireless device bandwidth results in new capabilities in terms of higher data rates and security. The 5G evolution is focus on exploiting larger bandwidths for higher though-puts. Interference and co-existence issues can also be addressed by the larger bandwidth in the 5G and 6G evolution. This dissertation introduces of a novel Ultra-wideband (UWB) Code Division Multiple Access (CDMA) technique to exploit the largest bandwidth available in the upcoming wireless connectivity scenarios. The dissertation addresses interference immunity, secure communication at the physical layer and longer distance communication due to increased receiver sensitivity. The dissertation presents the design, workflow, simulations, hardware prototypes and experimental measurements to demonstrate the benefits of wideband Code-Division-Multiple-Access. Specifically, a description of each of the hardware and software stages is presented along with simulations of different scenarios using a test-bench and open-field measurements. The measurements provided experimental validation carried out to demonstrate the interference mitigation capabilities. In addition, Direct RF sampling techniques are employed to handle the larger bandwidth and avoid analog components. Additionally, a transmit and receive chain is designed and implemented at 28 GHz to provide a proof-of-concept for future 5G applications. The proposed wideband transceiver is also used to demonstrate higher accuracy direction finding, as much as 10 times improvement