NoncovANM: Gridless DOA Estimation for LPDF System

Direction of arrival (DOA) estimation is an important research in the area of array signal processing, and has been studied for decades. High resolution DOA estimation requires large array aperture, which leads to the increase of hardware cost. Besides, high accuracy DOA estimation methods usually have high computational complexity. In this paper, the problem of decreasing the hardware cost and algorithm complexity is addressed. First, considering the ability of flexible controlling the electromagnetic waves and low-cost, an intelligent reconfigurable surface (IRS)-aided low-cost passive direction finding (LPDF) system is developed, where only one fully functional receiving channel is adopted. Then, the sparsity of targets direction in the spatial domain is exploited by formulating an atomic norm minimization (ANM) problem to estimate the DOA. Traditionally, solving ANM problem is complex and cannot be realized efficiently. Hence, a novel nonconvex-based ANM (NC-ANM) method is proposed by gradient threshold iteration, where a perturbation is introduced to avoid falling into saddle points. The theoretical analysis for the convergence of the NC-ANM method is also given. Moreover, the corresponding Cram\'er-Rao lower bound (CRLB) in the LPDF system is derived, and taken as the referred bound of the DOA estimation. Simulation results show that the proposed method outperforms the compared methods in the DOA estimation with lower computational complexity in the LPDF system.Comment: 11 pages, 8 figure

Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order

Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic $l_0$ norm (i.e., the continuous analog of $l_0$ norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of atomic $l_0$ norm, such as nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order by means of the atomic $l_0$ norm. To accomplish this idea, we build a multiobjective optimization model. The measurment error and the atomic $l_0$ norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic $l_0$ norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that help fully exploring the variable and open-ended frequency search space and provide extensive potentials towards the optima. Another innovation is a model order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model order selection.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Adaptive OFDM Radar for Target Detection and Tracking

We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing: OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar\u27s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the $ell_1$-constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose an tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter

Signals and Images in Sea Technologies

Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas

Advanced array signal processing algorithms for multi-dimensional parameter estimation

Multi-dimensional high-resolution parameter estimation is a fundamental problem in a variety of array signal processing applications, including radar, mobile communications, multiple-input multiple-output (MIMO) channel estimation, and biomedical imaging. The objective is to estimate the frequency parameters of noise-corrupted multi-dimensional harmonics that are sampled on a multi-dimensional grid. Among the proposed parameter estimation algorithms to solve this problem, multi-dimensional (R-D) ESPRIT-type algorithms have been widely used due to their computational efficiency and their simplicity. Their performance in various scenarios has been objectively evaluated by means of an analytical performance assessment framework. Recently, a relatively new class of parameter estimators based on sparse signal reconstruction has gained popularity due to their robustness under challenging conditions such as a small sample size or strong signal correlation. A common approach towards further improving the performance of parameter estimation algorithms is to exploit prior knowledge on the structure of the signals. In this thesis, we develop enhanced versions of R-D ESPRIT-type algorithms and the relatively new class of sparsity-based parameter estimation algorithms by exploiting the multi-dimensional structure of the signals and the statistical properties of strictly non-circular (NC) signals. First, we derive analytical expressions for the gain from forward-backward averaging and tensor-based processing in R-D ESPRIT-type and R-D Tensor-ESPRIT-type algorithms for the special case of two sources. This is accomplished by simplifying the generic analytical MSE expressions from the performance analysis of R-D ESPRIT-type algorithms. The derived expressions allow us to identify the parameter settings, e.g., the number of sensors, the signal correlation, and the source separation, for which both gains are most pronounced or no gain is achieved. Second, we propose the generalized least squares (GLS) algorithm to solve the overdetermined shift invariance equation in R-D ESPRIT-type algorithms. GLS directly incorporates the statistics of the subspace estimation error into the shift invariance solution through its covariance matrix, which is found via a first-order perturbation expansion. To objectively assess the estimation accuracy, we derive performance analysis expressions for the mean square error (MSE) of GLS-based ESPRIT-type algorithms, which are asymptotic in the effective SNR, i.e., the results become exact for a high SNR or a small sample size. Based on the performance analysis, we show that the simplified MSE expressions of GLS-based 1-D ESPRIT-type algorithms for a single source and two sources can be transformed into the corresponding Cramer-Rao bound (CRB) expressions, which provide a lower limit on the estimation error. Thereby, ESPRIT-type algorithms can become asymptotically efficient, i.e., they asymptotically achieve the CRB. Numerical simulations show that this can also be the case for more than two sources. In the third contribution, we derive matrix-based and tensor-based R-D NC ESPRIT-type algorithms for multi-dimensional strictly non-circular signals, where R-D NC Tensor-ESPRIT-type algorithms exploit both the multi-dimensional structure and the strictly non-circular structure of the signals. Exploiting the NC signal structure by means of a preprocessing step leads to a virtual doubling of the original sensor array, which provides an improved estimation accuracy and doubles the number of resolvable signals. We derive an analytical performance analysis and compute simplified MSE expressions for a single source and two sources. These expressions are used to analytically compute the NC gain for these cases, which has so far only been studied via Monte-Carlo simulations. We additionally consider spatial smoothing preprocessing for R-D ESPRIT-type algorithms, which has been widely used to improve the estimation performance for highly correlated signals or a small sample size. Once more, we derive performance analysis expressions for R-D ESPRIT-type algorithms and their corresponding NC versions with spatial smoothing and derive the optimal number of subarrays for spatial smoothing that minimizes the MSE for a single source. In the next part, we focus on the relatively new concept of parameter estimation via sparse signal reconstruction (SSR), in which the sparsity of the received signal power spectrum in the spatio-temporal domain is exploited. We develop three NC SSR-based parameter estimation algorithms for strictly noncircular sources and show that the benefits of exploiting the signals’ NC structure can also be achieved via sparse reconstruction. We develop two grid-based NC SSR algorithms with a low-complexity off-grid estimation procedure, and a gridless NC SSR algorithm based on atomic norm minimization. As the final contribution of this thesis, we derive the deterministic R-D NC CRB for strictly non-circular sources, which serves as a benchmark for the presented R-D NC ESPRIT-type algorithms and the NC SSR-based parameter estimation algorithms. We show for the special cases of, e.g., full coherence, a single snapshot, or a single strictly non-circular source, that the deterministic R-D NC CRB reduces to the existing deterministic R-D CRB for arbitrary signals. Therefore, no NC gain can be achieved in these cases. For the special case of two closely-spaced NC sources, we simplify the NC CRB expression and compute the NC gain for two closely-spaced NC signals. Finally, its behavior in terms of the physical parameters is studied to determine the parameter settings that provide the largest NC gain.Die hochauflösende Parameterschätzung für mehrdimensionale Signale findet Anwendung in vielen Bereichen der Signalverarbeitung in Mehrantennensystemen. Zu den Anwendungsgebieten zählen beispielsweise Radar, die Mobilkommunikation, die Kanalschätzung in multiple-input multiple-output (MIMO)-Systemen und bildgebende Verfahren in der Biosignalverarbeitung. In letzter Zeit sind eine Vielzahl von Algorithmen zur Parameterschätzung entwickelt worden, deren Schätzgenauigkeit durch eine analytische Beschreibung der Leistungsfähigkeit objektiv bewertet werden kann. Eine verbreitete Methode zur Verbesserung der Schätzgenauigkeit von Parameterschätzverfahren ist die Ausnutzung von Vorwissen bezüglich der Signalstruktur. In dieser Arbeit werden mehrdimensionale ESPRIT-Verfahren als Beispiel für Unterraum-basierte Verfahren entwickelt und analysiert, die explizit die mehrdimensionale Signalstruktur mittels Tensor-Signalverarbeitung ausnutzt und die statistischen Eigenschaften von nicht-zirkulären Signalen einbezieht. Weiterhin werden neuartige auf Signalrekonstruktion basierende Algorithmen vorgestellt, die die nicht-zirkuläre Signalstruktur bei der Rekonstruktion ausnutzen. Die vorgestellten Verfahren ermöglichen eine deutlich verbesserte Schätzgüte und verdoppeln die Anzahl der auflösbaren Signale. Die Vielzahl der Forschungsbeiträge in dieser Arbeit setzt sich aus verschiedenen Teilen zusammen. Im ersten Teil wird die analytische Beschreibung der Leistungsfähigkeit von Matrix-basierten und Tensor-basierten ESPRIT-Algorithmen betrachtet. Die Tensor-basierten Verfahren nutzen explizit die mehrdimensionale Struktur der Daten aus. Es werden für beide Algorithmenarten vereinfachte analytische Ausdrücke für den mittleren quadratischen Schätzfehler für zwei Signalquellen hergeleitet, die lediglich von den physikalischen Parametern, wie zum Beispiel die Anzahl der Antennenelemente, das Signal-zu-Rausch-Verhältnis, oder die Anzahl der Messungen, abhängen. Ein Vergleich dieser Ausdrücke ermöglicht die Berechnung einfacher Ausdrücke für den Schätzgenauigkeitsgewinn durch den forward-backward averaging (FBA)-Vorverarbeitungsschritt und die Tensor-Signalverarbeitung, die die analytische Abhängigkeit von den physikalischen Parametern enthalten. Im zweiten Teil entwickeln wir einen neuartigen general least squares (GLS)-Ansatz zur Lösung der Verschiebungs-Invarianz-Gleichung, die die Grundlage der ESPRIT-Algorithmen darstellt. Der neue Lösungsansatz berücksichtigt die statistische Beschreibung des Fehlers bei der Unterraumschätzung durch dessen Kovarianzmatrix und ermöglicht unter bestimmten Annahmen eine optimale Lösung der Invarianz-Gleichung. Mittels einer Performanzanalyse der GLS-basierten ESPRIT-Verfahren und der Vereinfachung der analytischen Ausdrücke für den Schätzfehler für eine Signalquelle und zwei zeitlich unkorrelierte Signalquellen wird gezeigt, dass die Cramer-Rao-Schranke, eine untere Schranke für die Varianz eines Schätzers, erreicht werden kann. Im nächsten Teil werden Matrix-basierte und Tensor-basierte ESPRIT-Algorithmen für nicht-zirkuläre Signalquellen vorgestellt. Unter Ausnutzung der Signalstruktur gelingt es, die Schätzgenauigkeit zu erhöhen und die doppelte Anzahl an Quellen aufzulösen. Dabei ermöglichen die vorgeschlagenen Tensor-ESPRIT-Verfahren sogar die gleichzeitige Ausnutzung der mehrdimensionalen Signalstruktur und der nicht-zirkuläre Signalstruktur. Die Leistungsfähigkeit dieser Verfahren wird erneut durch eine analytische Beschreibung objektiv bewertet und Spezialfälle für eine und zwei Quellen betrachtet. Es zeigt sich, dass für eine Quelle keinerlei Gewinn durch die nicht-zirkuläre Struktur erzielen lässt. Für zwei nicht-zirkuläre Quellen werden vereinfachte Ausdrücke für den Gewinn sowohl im Matrixfall also auch im Tensorfall hergeleitet und die Abhängigkeit der physikalischen Parameter analysiert. Sind die Signale stark korreliert oder ist die Anzahl der Messdaten sehr gering, kann der spatial smoothing-Vorverarbeitungsschritt mit den verbesserten ESPRIT-Verfahren kombiniert werden. Anhand der Performanzanalyse wird die Anzahl der Mittellungen für das spatial smoothing-Verfahren analytisch für eine Quelle bestimmt, die den Schätzfehler minimiert. Der nächste Teil befasst sich mit einer vergleichsweise neuen Klasse von Parameterschätzverfahren, die auf der Rekonstruktion überlagerter dünnbesetzter Signale basiert. Als Vorteil gegenüber den Algorithmen, die eine Signalunterraumschätzung voraussetzen, sind die Rekonstruktionsverfahren verhältnismäßig robust im Falle einer geringen Anzahl zeitlicher Messungen oder einer starken Korrelation der Signale. In diesem Teil der vorliegenden Arbeit werden drei solcher Verfahren entwickelt, die bei der Rekonstruktion zusätzlich die nicht-zirkuläre Signalstruktur ausnutzen. Dadurch kann auch für diese Art von Verfahren eine höhere Schätzgenauigkeit erreicht werden und eine höhere Anzahl an Signalen rekonstruiert werden. Im letzten Kapitel der Arbeit wird schließlich die Cramer-Rao-Schranke für mehrdimensionale nicht-zirkuläre Signale hergeleitet. Sie stellt eine untere Schranke für den Schätzfehler aller Algorithmen dar, die speziell für die Ausnutzung dieser Signalstruktur entwickelt wurden. Im Vergleich zur bekannten Cramer-Rao-Schranke für beliebige Signale, zeigt sich, dass im Fall von zeitlich kohärenten Signalen, für einen Messvektor oder für eine Quelle, beide Schranken äquivalent sind. In diesen Fällen kann daher keine Verbesserung der Schätzgüte erzielt werden. Zusätzlich wird die Cramer-Rao-Schranke für zwei benachbarte nicht-zirkuläre Signalquellen vereinfacht und der maximal mögliche Gewinn in Abhängigkeit der physikalischen Parameter analytisch ermittelt. Dieser Ausdruck gilt als Maßstab für den erzielbaren Gewinn aller Parameterschätzverfahren für zwei nicht-zirkuläre Signalquellen

Opportunistic communications in large uncoordinated networks

(English) The increase of wireless devices offering high data rate services limits the coexistence of wireless systems sharing the same resources in a given geographical area because of inter-system interference. Therefore, interference management plays a key role in permitting the coexistence of several heterogeneous communication services. However, classical interference management strategies require lateral information giving rise to the need for inter-system coordination and cooperation, which is not always practical. Opportunistic communications offer a potential solution to the problem of inter-system interference management. The basic principle of opportunistic communications is to efficiently and robustly exploit the resources available in a wireless network and adapt the transmitted signals to the state of the network to avoid inter-system interference. Therefore, opportunistic communications depend on inferring the available network resources that can be safely exploited without inducing interference in coexisting communication nodes. Once the available network resources are identified, the most prominent opportunistic communication techniques consist in designing scenario-adapted precoding/decoding strategies to exploit the so-called null space. Despite this, classical solutions in the literature suffer from two main drawbacks: the lack of robustness to detection errors and the need for intra-system cooperation. This thesis focuses on the design of a null space-based opportunistic communication scheme that addresses the drawbacks exhibited by existing methodologies under the assumption that opportunistic nodes do not cooperate. For this purpose, a generalized detection error model independent of the null-space identification mechanism is introduced that allows the design of solutions that exhibit minimal inter-system interference in the worst case. These solutions respond to a maximum signal-to-interference ratio (SIR) criterion, which is optimal under non-cooperative conditions. The proposed methodology allows the design of a family of orthonormal waveforms that perform a spreading of the modulated symbols within the detected null space, which is key to minimizing the induced interference density. The proposed solutions are invariant within the inferred null space, allowing the removal of the feedback link without giving up coherent waveform detection. In the absence of coordination, the waveform design relies solely on locally sensed network state information, inducing a mismatch between the null spaces identified by the transmitter and receiver that may worsen system performance. Although the proposed solution is robust to this mismatch, the design of enhanced receivers using active subspace detection schemes is also studied. When the total number of network resources increases arbitrarily, the proposed solutions tend to be linear combinations of complex exponentials, providing an interpretation in the frequency domain. This asymptotic behavior allows us to adapt the proposed solution to frequency-selective channels by means of a cyclic prefix and to study an efficient modulation similar to the time division multiplexing scheme but using circulant waveforms. Finally, the impact of the use of multiple antennas in opportunistic null space-based communications is studied. The performed analysis reveals that, in any case, the structure of the antenna clusters affects the opportunistic communication, since the proposed waveform mimics the behavior of a single-antenna transmitter. On the other hand, the number of sensors employed translates into an improvement in terms of SIR.(Català) El creixement incremental dels dispositius sense fils que requereixen serveis d'alta velocitat de dades limita la coexistència de sistemes sense fils que comparteixen els mateixos recursos en una àrea geogràfica donada a causa de la interferència entre sistemes. Conseqüentment, la gestió d'interferència juga un paper fonamental per a facilitar la coexistència de diversos serveis de comunicació heterogenis. No obstant això, les estratègies clàssiques de gestió d'interferència requereixen informació lateral originant la necessitat de coordinació i cooperació entre sistemes, que no sempre és pràctica. Les comunicacions oportunistes ofereixen una solució potencial al problema de la gestió de les interferències entre sistemes. El principi bàsic de les comunicacions oportunistes és explotar de manera eficient i robusta els recursos disponibles en una xarxa sense fils i adaptar els senyals transmesos a l'estat de la xarxa per evitar interferències entre sistemes. Per tant, les comunicacions oportunistes depenen de la inferència dels recursos de xarxa disponibles que poden ser explotats de manera segura sense induir interferència en els nodes de comunicació coexistents. Una vegada que s'han identificat els recursos de xarxa disponibles, les tècniques de comunicació oportunistes més prominents consisteixen en el disseny d'estratègies de precodificació/descodificació adaptades a l'escenari per explotar l'anomenat espai nul. Malgrat això, les solucions clàssiques en la literatura sofreixen dos inconvenients principals: la falta de robustesa als errors de detecció i la necessitat de cooperació intra-sistema. Aquesta tesi tracta el disseny d'un esquema de comunicació oportunista basat en l'espai nul que afronta els inconvenients exposats per les metodologies existents assumint que els nodes oportunistes no cooperen. Per a aquest propòsit, s'introdueix un model generalitzat d'error de detecció independent del mecanisme d'identificació de l'espai nul que permet el disseny de solucions que exhibeixen interferències mínimes entre sistemes en el cas pitjor. Aquestes solucions responen a un criteri de màxima relació de senyal a interferència (SIR), que és òptim en condicions de no cooperació. La metodologia proposada permet dissenyar una família de formes d'ona ortonormals que realitzen un spreading dels símbols modulats dins de l'espai nul detectat, que és clau per minimitzar la densitat d’interferència induïda. Les solucions proposades són invariants dins de l'espai nul inferit, permetent suprimir l'enllaç de retroalimentació i, tot i així, realitzar una detecció coherent de forma d'ona. Sota l’absència de coordinació, el disseny de la forma d'ona es basa únicament en la informació de l'estat de la xarxa detectada localment, induint un desajust entre els espais nuls identificats pel transmissor i receptor que pot empitjorar el rendiment del sistema. Tot i que la solució proposada és robusta a aquest desajust, també s'estudia el disseny de receptors millorats fent ús de tècniques de detecció de subespai actiu. Quan el nombre total de recursos de xarxa augmenta arbitràriament, les solucions proposades tendeixen a ser combinacions lineals d'exponencials complexes, proporcionant una interpretació en el domini freqüencial. Aquest comportament asimptòtic permet adaptar la solució proposada a entorns selectius en freqüència fent ús d'un prefix cíclic i estudiar una modulació eficient derivada de l'esquema de multiplexat per divisió de temps emprant formes d'ona circulant. Finalment, s’estudia l'impacte de l'ús de múltiples antenes en comunicacions oportunistes basades en l'espai nul. L'anàlisi realitzada permet concloure que, en cap cas, l'estructura de les agrupacions d'antenes tenen un impacte sobre la comunicació oportunista, ja que la forma d'ona proposada imita el comportament d'un transmissor mono-antena. D'altra banda, el nombre de sensors emprat es tradueix en una millora en termes de SIR.(Español) El incremento de los dispositivos inalámbricos que ofrecen servicios de alta velocidad de datos limita la coexistencia de sistemas inalámbricos que comparten los mismos recursos en un área geográfica dada a causa de la interferencia inter-sistema. Por tanto, la gestión de interferencia juega un papel fundamental para facilitar la coexistencia de varios servicios de comunicación heterogéneos. Sin embargo, las estrategias clásicas de gestión de interferencia requieren información lateral originando la necesidad de coordinación y cooperación entre sistemas, que no siempre es práctica. Las comunicaciones oportunistas ofrecen una solución potencial al problema de la gestión de las interferencias entre sistemas. El principio básico de las comunicaciones oportunistas es explotar de manera eficiente y robusta los recursos disponibles en una red inalámbricas y adaptar las señales transmitidas al estado de la red para evitar interferencias entre sistemas. Por lo tanto, las comunicaciones oportunistas dependen de la inferencia de los recursos de red disponibles que pueden ser explotados de manera segura sin inducir interferencia en los nodos de comunicación coexistentes. Una vez identificados los recursos disponibles, las técnicas de comunicación oportunistas más prominentes consisten en el diseño de estrategias de precodificación/descodificación adaptadas al escenario para explotar el llamado espacio nulo. A pesar de esto, las soluciones clásicas en la literatura sufren dos inconvenientes principales: la falta de robustez a los errores de detección y la necesidad de cooperación intra-sistema. Esta tesis propone diseñar un esquema de comunicación oportunista basado en el espacio nulo que afronta los inconvenientes expuestos por las metodologías existentes asumiendo que los nodos oportunistas no cooperan. Para este propósito, se introduce un modelo generalizado de error de detección independiente del mecanismo de identificación del espacio nulo que permite el diseño de soluciones que exhiben interferencias mínimas entre sistemas en el caso peor. Estas soluciones responden a un criterio de máxima relación de señal a interferencia (SIR), que es óptimo en condiciones de no cooperación. La metodología propuesta permite diseñar una familia de formas de onda ortonormales que realizan un spreading de los símbolos modulados dentro del espacio nulo detectado, que es clave para minimizar la densidad de interferencia inducida. Las soluciones propuestas son invariantes dentro del espacio nulo inferido, permitiendo suprimir el enlace de retroalimentación sin renunciar a la detección coherente de forma de onda. En ausencia de coordinación, el diseño de la forma de onda se basa únicamente en la información del estado de la red detectada localmente, induciendo un desajuste entre los espacios nulos identificados por el transmisor y receptor que puede empeorar el rendimiento del sistema. A pesar de que la solución propuesta es robusta a este desajuste, también se estudia el diseño de receptores mejorados usando técnicas de detección de subespacio activo. Cuando el número total de recursos de red aumenta arbitrariamente, las soluciones propuestas tienden a ser combinaciones lineales de exponenciales complejas, proporcionando una interpretación en el dominio frecuencial. Este comportamiento asintótico permite adaptar la solución propuesta a canales selectivos en frecuencia mediante un prefijo cíclico y estudiar una modulación eficiente derivada del esquema de multiplexado por división de tiempo empleando formas de onda circulante. Finalmente, se estudia el impacto del uso de múltiples antenas en comunicaciones oportunistas basadas en el espacio nulo. El análisis realizado revela que la estructura de las agrupaciones de antenas no afecta la comunicación oportunista, ya que la forma de onda propuesta imita el comportamiento de un transmisor mono-antena. Por otro lado, el número de sensores empleado se traduce en una mejora en términos de SIR.Postprint (published version

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Indoor Localisation of Scooters from Ubiquitous Cost-Effective Sensors: Combining Wi-Fi, Smartphone and Wheel Encoders

Indoor localisation of people and objects has been a focus of research studies for several decades because of its great advantage to several applications. Accuracy has always been a challenge because of the uncertainty of the employed sensors. Several technologies have been proposed and researched, however, accuracy still represents an issue. Today, several sensor technologies can be found in indoor environments, some of which are economical and powerful, such as Wi-Fi. Meanwhile, Smartphones are typically present indoors because of the people that carry them along, while moving about within rooms and buildings. Furthermore, vehicles such as mobility scooters can also be present indoor to support people with mobility impairments, which may be equipped with low-cost sensors, such as wheel encoders. This thesis investigates the localisation of mobility scooters operating indoor. This represents a specific topic as most of today's indoor localisation systems are for pedestrians. Furthermore, accurate indoor localisation of those scooters is challenging because of the type of motion and specific behaviour. The thesis focuses on improving localisation accuracy for mobility scooters and on the use of already available indoor sensors. It proposes a combined use of Wi-Fi, Smartphone IMU and wheel encoders, which represents a cost-effective energy-efficient solution. A method has been devised and a system has been developed, which has been experimented on different environment settings. The outcome of the experiments are presented and carefully analysed in the thesis. The outcome of several trials demonstrates the potential of the proposed solutions in reducing positional errors significantly when compared to the state-of-the-art in the same area. The proposed combination demonstrated an error range of 0.35m - 1.35m, which can be acceptable in several applications, such as some related to assisted living. 3 As the proposed system capitalizes on the use of ubiquitous technologies, it opens up to a potential quick take up from the market, therefore being of great benefit for the target audience