This thesis proposes, validates, and compares low-complexity algorithms for the fast-and-safe charge and balance of Li-ion batteries both for the single cell case and for the case of a serially-connected string of battery cells. The proposed algorithms are based on a reduced-order electrochemical model (Equivalent Hydraulic Model, EHM), and make use of constrained-control strategies to limit the main electrochemical degradation phenomena that may accelerate aging, namely: Lithium plating in the anode and solvent oxidation inthe cathode. To avoid the computational intensiveness of solving an online optimization as in the Model Predictive Control (MPC) framework, this thesis proposes the use of Reference Governor schemes. Variants of both the Scalar Reference Governors (SRG) and the Explicit Reference Governors (ERG) are developed to deal with the non-convex admissible region for the charge of a battery cell, while keeping a low computational burden. To evaluate the performance of the proposed techniques for the single cell case, they are experimentallyvalidated on commercial Turnigy LCO cells of 160 mAh at four different constant temperatures (10, 20, 30 and 40 °C). In the second part of this thesis, the proposed charging strategies are extended to take into account the balance of a serially-connected string of cells. To equalize possible mismatches, a centralized policy based on a shunting grid (active balance) connects or disconnects the cells during the charge. After a preliminary analysis, a simple mixed-integer algorithm was proposed. Since this method is computationally inefficient due to the high number of scenarios to be evaluated, this thesis proposes a ratio-based algorithm based on a Pulse-Width Modulation (PWM) approach. This approach can be used within both MPC and RG schemes. The numerical validations of the proposed algorithms for the case of a string of four battery cells are carried out using a simulator based on a full-order electrochemical model. Numerical validations show that the PWM-like approach charges in parallel all the cells within the pack, whereas the mixed-integer approach charges the battery cells sequentially from the battery cell with the lowest state of charge to the ones with the highest states of charge. On the basis of the simulations, an algorithm based on a mixed logic that allows to charge in a “sequential parallel” approach is proposed. Some conclusions and future directions of research are proposed at the end of the thesis. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

No description available.

Vision systems have become ubiquitous. They are used for traffic monitoring, elderly care, video conferencing, virtual reality, surveillance, smart rooms, home automation, sport games analysis, industrial safety, medical care etc. In most vision systems, the data coming from the visual sensor(s) is processed before transmission in order to save communication bandwidth or achieve higher frame rates. The type of data processing needs to be chosen carefully depending on the targeted application, and taking into account the available memory, computational power, energy resources and bandwidth constraints. In this dissertation, we investigate how a vision system should be built under practical constraints. First, this system should be intelligent, such that the right data is extracted from the video source. Second, when processing video data this intelligent vision system should know its own practical limitations, and should try to achieve the best possible output result that lies within its capabilities. We study and improve a wide range of vision systems for a variety of applications, which go together with different types of constraints. First, we present a modulo-PCM-based coding algorithm for applications that demand very low complexity coding and need to preserve some of the advantageous properties of PCM coding (direct processing, random access, rate scalability). Our modulo-PCM coding scheme combines three well-known, simple, source coding strategies: PCM, binning, and interpolative coding. The encoder first analyzes the signal statistics in a very simple way. Then, based on these signal statistics, the encoder simply discards a number of bits of each image sample. The modulo-PCM decoder recovers the removed bits of each sample by using its received bits and side information which is generated by interpolating previous decoded signals. Our algorithm is especially appropriate for image coding. / Morbee, M. (2011). Optimized information processing in resource-constrained vision systems. From low-complexity coding to smart sensor networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12126

Nowadays, the digital terrestrial television (DTT) market is characterized by the high capacity needed for high definition TV services. There is a need for an efficient use of the broadcast spectrum, which requires new technologies to guarantee increased capacities. Non-Uniform Constellations (NUC) arise as one of the most innovative techniques to approach those requirements. NUCs reduce the gap between uniform Gray-labelled Quadrature Amplitude Modulation (QAM) constellations and the theoretical unconstrained Shannon limit. With these constellations, symbols are optimized in both in-phase (I) and quadrature (Q) components by means of signal geometrical shaping, considering a certain signal-to-noise ratio (SNR) and channel model. There are two types of NUC, one-dimensional and two-dimensional NUCs (1D-NUC and 2D-NUC, respectively). 1D-NUCs maintain the squared shape from QAM, but relaxing the distribution between constellation symbols in a single component, with non-uniform distance between them. These constellations provide better SNR performance than QAM, without any demapping complexity increase. 2D-NUCs also relax the square shape constraint, allowing to optimize the symbol positions in both dimensions, thus achieving higher capacity gains and lower SNR requirements. However, the use of 2D-NUCs implies a higher demapping complexity, since a 2D-demapper is needed, i.e. I and Q components cannot be separated. In this dissertation, NUCs are analyzed from both transmit and receive point of views, using either single-input single-output (SISO) or multiple-input multiple-output (MIMO) antenna configurations. In SISO transmissions, 1D-NUCs and 2D-NUCs are optimized for a wide range of SNRs and different constellation orders. The optimization of rotated 2D-NUCs is also investigated. Even though the demapping complexity is not increased, the SNR gain of these constellations is not significant. The highest rotation gain is obtained for low-order constellations and high SNRs. However, with multi-RF techniques, the SNR gain is drastically increased, since I and Q components are transmitted in different RF channels. In this thesis, multi-RF gains of NUCs with and without rotation are provided for some representative scenarios. At the receiver, two different implementation bottlenecks are explored. First, the demapping complexity of all considered constellations is analyzed. Afterwards, two complexity reduction algorithms for 2D-NUCs are proposed. Both algorithms drastically reduce the number of distances to compute. Moreover, both are finally combined in a single demapper. Quantization of NUCs is also explored in this dissertation, since LLR values and I/Q components are modified when using these constellations, compared to traditional QAM constellations. A new algorithm that is based on the optimization of the quantizer levels for a particular constellation is proposed. The use of NUCs in multi-antenna communications is also investigated. It includes the optimization in one or two antennas, the use of power imbalance, the cross-polar discrimination (XPD) between receive antennas, or the use of different demappers. Assuming different values for the parameters evaluated, new Multi-Antenna Non-Uniform Constellations (MA-NUC) are obtained by means of a particularized re-optimization process, specific for MIMO. At the receiver, an extended demapping complexity analysis is performed, where it is shown that the use of 2D-NUCs in MIMO extremely increases the demapping complexity. As an alternative, an efficient solution for 2D-NUCs and MIMO systems based on Soft-Fixed Sphere Decoding (SFSD) is proposed. The main drawback is that SFSD demappers do not work with 2D-NUCs, since they perform a Successive Interference Cancellation (SIC) step that needs to be performed in separated I and Q components. The proposed method quantifies the closest symbol using Voronoi regions and allows SFSD demappers to work. / Hoy en día, el mercado de la televisión digital terrestre (TDT) está caracterizado por la alta capacidad requerida para transmitir servicios de televisión de alta definición y el espectro disponible. Es necesario por tanto un uso eficiente del espectro radioeléctrico, el cual requiere nuevas tecnologías para garantizar mayores capacidades. Las constelaciones no-uniformes (NUC) emergen como una de las técnicas más innovadoras para abordar tales requerimientos. Las NUC reducen el espacio existente entre las constelaciones uniformes QAM y el límite teórico de Shannon. Con estas constelaciones, los símbolos se optimizan en ambas componentes fase (I) y cuadratura (Q) mediante técnicas geométricas de modelado de la señal, considerando un nivel señal a ruido (SNR) concreto y un modelo de canal específico. Hay dos tipos de NUC, unidimensionales y bidimensionales (1D-NUC y 2D-NUC, respectivamente). Las 1D-NUC mantienen la forma cuadrada de las QAM, pero permiten cambiar la distribución entre los símbolos en una componente concreta, teniendo una distancia no uniforme entre ellos. Estas constelaciones proporcionan un mejor rendimiento SNR que QAM, sin ningún incremento en la complejidad en el demapper. Las 2D-NUC también permiten cambiar la forma cuadrada de la constelación, permitiendo optimizar los símbolos en ambas dimensiones y por tanto obteniendo mayores ganancias en capacidad y menores requerimientos en SNR. Sin embargo, el uso de 2D-NUCs implica una mayor complejidad en el receptor. En esta tesis se analizan las NUC desde el punto de vista tanto de transmisión como de recepción, utilizando bien configuraciones con una antena (SISO) o con múltiples antenas (MIMO). En transmisiones SISO, se han optimizado 1D-NUCs para un rango amplio de distintas SNR y varios órdenes de constelación. También se ha investigado la optimización de 2D-NUCs rotadas. Aunque la complejidad no aumenta, la ganancia SNR de estas constelaciones no es significativa. La mayor ganancia por rotación se obtiene para bajos órdenes de constelación y altas SNR. Sin embargo, utilizando técnicas multi-RF, la ganancia aumenta drásticamente puesto que las componentes I y Q se transmiten en distintos canales RF. En esta tesis, se han estudiado varias ganancias multi-RF representativas de las NUC, con o sin rotación. En el receptor, se han identificado dos cuellos de botella diferentes en la implementación. Primero, se ha analizado la complejidad en el receptor para todas las constelaciones consideradas y, posteriormente, se proponen dos algoritmos para reducir la complejidad con 2D-NUCs. Además, los dos pueden combinarse en un único demapper. También se ha explorado la cuantización de estas constelaciones, ya que tanto los valores LLR como las componentes I/Q se ven modificados, comparando con constelaciones QAM tradicionales. Además, se ha propuesto un algoritmo que se basa en la optimización para diferentes niveles de cuantización, para una NUC concreta. Igualmente, se ha investigado en detalle el uso de NUCs en MIMO. Se ha incluido la optimización en una sola o en dos antenas, el uso de un desbalance de potencia, factores de discriminación entre antenas receptoras (XPD), o el uso de distintos demappers. Asumiendo distintos valores, se han obtenido nuevas constelaciones multi-antena (MA-NUC) gracias a un nuevo proceso de re-optimización específico para MIMO. En el receptor, se ha extendido el análisis de complejidad en el demapper, la cual se incrementa enormemente con el uso de 2D-NUCs y sistemas MIMO. Como alternativa, se propone una solución basada en el algoritmo Soft-Fixed Sphere Decoding (SFSD). El principal problema es que estos demappers no funcionan con 2D-NUCs, puesto que necesitan de un paso adicional en el que las componentes I y Q necesitan separarse. El método propuesto cuantifica el símbolo más cercano utilizando las regiones de Voronoi, permitiendo el uso de este tipo de receptor. / Actualment, el mercat de la televisió digital terrestre (TDT) està caracteritzat per l'alta capacitat requerida per a transmetre servicis de televisió d'alta definició i l'espectre disponible. És necessari per tant un ús eficient de l'espectre radioelèctric, el qual requereix noves tecnologies per a garantir majors capacitats i millors servicis. Les constel·lacions no-uniformes (NUC) emergeixen com una de les tècniques més innovadores en els sistemes de televisió de següent generació per a abordar tals requeriments. Les NUC redueixen l'espai existent entre les constel·lacions uniformes QAM i el límit teòric de Shannon. Amb estes constel·lacions, els símbols s'optimitzen en ambdós components fase (I) i quadratura (Q) per mitjà de tècniques geomètriques de modelatge del senyal, considerant un nivell senyal a soroll (SNR) concret i un model de canal específic. Hi ha dos tipus de NUC, unidimensionals i bidimensionals (1D-NUC i 2D-NUC, respectivament). 1D-NUCs mantenen la forma quadrada de les QAM, però permet canviar la distribució entre els símbols en una component concreta, tenint una distància no uniforme entre ells. Estes constel·lacions proporcionen un millor rendiment SNR que QAM, sense cap increment en la complexitat al demapper. 2D-NUC també canvien la forma quadrada de la constel·lació, permetent optimitzar els símbols en ambdós dimensions i per tant obtenint majors guanys en capacitat i menors requeriments en SNR. No obstant això, l'ús de 2D-NUCs implica una major complexitat en el receptor, ja que es necessita un demapper 2D, on les components I i Q no poden ser separades. En esta tesi s'analitzen les NUC des del punt de vista tant de transmissió com de recepció, utilitzant bé configuracions amb una antena (SISO) o amb múltiples antenes (MIMO). En transmissions SISO, s'han optimitzat 1D-NUCs, per a un rang ampli de distintes SNR i diferents ordes de constel·lació. També s'ha investigat l'optimització de 2D-NUCs rotades. Encara que la complexitat no augmenta, el guany SNR d'estes constel·lacions no és significativa. El major guany per rotació s'obté per a baixos ordes de constel·lació i altes SNR. No obstant això, utilitzant tècniques multi-RF, el guany augmenta dràsticament ja que les components I i Q es transmeten en distints canals RF. En esta tesi, s'ha estudiat el guany multi-RF de les NUC, amb o sense rotació. En el receptor, s'han identificat dos colls de botella diferents en la implementació. Primer, s'ha analitzat la complexitat en el receptor per a totes les constel·lacions considerades i, posteriorment, es proposen dos algoritmes per a reduir la complexitat amb 2D-NUCs. Ambdós algoritmes redueixen dràsticament el nombre de distàncies. A més, els dos poden combinar-se en un únic demapper. També s'ha explorat la quantització d'estes constel·lacions, ja que tant els valors LLR com les components I/Q es veuen modificats, comparant amb constel·lacions QAM tradicionals. A més, s'ha proposat un algoritme que es basa en l'optimització per a diferents nivells de quantització, per a una NUC concreta. Igualment, s'ha investigat en detall l'ús de NUCs en MIMO. S'ha inclòs l'optimització en una sola o en dos antenes, l'ús d'un desbalanç de potència, factors de discriminació entre antenes receptores (XPD), o l'ús de distints demappers. Assumint distints valors, s'han obtingut noves constel·lacions multi-antena (MA-NUC) gràcies a un nou procés de re-optimització específic per a MIMO. En el receptor, s'ha modificat l'anàlisi de complexitat al demapper, la qual s'incrementa enormement amb l'ús de 2D-NUCs i sistemes MIMO. Com a alternativa, es proposa una solució basada en l'algoritme Soft-Fixed Sphere Decoding (SFSD) . El principal problema és que estos demappers no funcionen amb 2D-NUCs, ja que necessiten d'un pas addicional en què les components I i Q necessiten separar-se. El mètode proposat quantifica el símbol més pròxim utilitzan / Fuentes Muela, M. (2017). Non-Uniform Constellations for Next-Generation Digital Terrestrial Broadcast Systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84743

Systems with multiple transmitting and receiving antennas in large-scale (LS-MIMO - large-scale multipleinput multiple-output) enable high spectral and energy efficiency gains, which results in an increase in the data transmission rate in the same band, without increasing the transmitted power per user. In addition, with the increase of the number of antennas in the base station (BS) it is possible to attend to a larger number of users per cell, in the same occupied band. Furthermore, it has been found in the literature that the reported advantages of LS-MIMO systems can be obtained with a large number of antennas on at least one side of the communication, usually in BS due to physical restriction in user equipments. However, such advantages have their cost: the use of a large number of antennas also difficult tasks involving signal processing, such as estimation of channel coefficients, precoding and signal detection. It is at this juncture that this Doctoral Thesis is developed, in which the computational complexity of performing efficient detection methods in LSMIMO communication systems is explored through the analysis of algorithms and optimization techniques in the solution of specific problems and still open. More precisely, this Thesis discusses and proposes promising detection techniques in LS-MIMO systems, aiming to improve performance metrics - in terms of error rate - and computational complexity - in terms of the number of mathematical operations. Initially, the problem is introduced through a conventional MIMO system model, where channels with imperfect estimates and correlation between transmitter (Tx) and receiver (Rx) antennas are considered. Preprocessing techniques based on lattice reduction (LR) are applied in linear detectors, in addition to the sphere decoder (SD), which proposes a lookup table procedure in order to provide a reduction in computational complexity. It is shown that the LR method in the pre-detection results in a significant performance gain in both the condition of uncorrelated and correlated channels, and in the latter scenario the improvement is even more remarkable due to the diversity gain provided. On the other hand, the complexity involved in the application of LR in high correlation scenarios becomes preponderant in linear detectors. In the LR-SD using the lookup table procedure, the optimum gain was reached in all scenarios, as expected, and resulted in a lower complexity than maximum likelihood (ML) detector, even with maximum correlation between antennas, which represents the most complex scenario for the LR technique. Next, the message passing (MP) detector is investigated, which makes use of Markov random fields (MRF) and factor graph (FG) graphical models. Moreover, it is shown in the literature that the message damping (MD) method applied to the MRF detector brings relevant performance gain without increasing computational complexity. On the other hand, the DF value is specified for only a restricted range of scenarios. Numerical results are extensively generated, in order to obtain a range of analysis of the MRF with MD, which resulted in the proposition of an optimal value for the DF, based on numerical curve fitting. Finally, in the face of the MGS detector, two approaches are proposed to reduce the negative impact caused by the random solution when high modulation orders are employed. The first is based on an average between multiple samples, called aMGS (averaged MGS). The second approach deploys a direct restriction on the range of the random solution, limiting in d the neighborhood of symbols that can be sorted, being called d-sMGS. Numerical simulation results show that both approaches result in gain of convergence in relation to MGS, especially: in regions of high system loading, d-sMGS detection demonstrated significant gain in both performance and complexity compared to aMGS and MGS; although in low-medium loading, the aMGS strategy showed less complexity, with performance marginally similar to the others. Furthermore, it is concluded that increasing the dimensions of the system favors a smaller restriction in the neighborhood. / Sistemas com múltiplas antenas transmissoras e múltiplas antenas receptoras em larga escala (LS-MIMO - large-scale multiple-input multiple-output) possibilitam altos ganhos em eficiência espectral e energética, o que resulta em aumento da taxa de transmissão de dados numa mesma banda ocupada, sem acréscimo da potência transmitida por usuário. Além disso, com o aumento do número de antenas na estação rádio-base (BS- base station) possibilita-se o atendimento de maior número de usuários por célula, em uma mesma banda ocupada. Ademais, comprovou-se na literatura que as vantagens relatadas dos sistemas LS-MIMO podem ser obtidas com um grande número de antenas em, pelo menos, um dos lados da comunicação, geralmente na BS devido à restrição física nos dispositivos móveis. Contudo, tais vantagens têm seu custo: a utilização de um grande número de antenas também dificulta tarefas que envolvem processamento de sinais, como estimação dos coeficientes de canal, precodificação e detecção de sinais. É nessa conjuntura em que se desenvolve esta Tese de Doutorado, na qual se explora o compromisso desempenho versus complexidade computacional de métodos eficientes de detecção em sistemas de comunicações LS-MIMO através da análise de algoritmos e técnicas de otimização na solução de problemas específicos e ainda em aberto. Mais precisamente, a presente Tese discute e propõe técnicas promissoras de detecção em sistemas LS-MIMO, visando a melhoria de métricas de desempenho - em termos de taxa de erro - e complexidade computacional - em termos de quantidade de operações matemáticas. Inicialmente, o problema é introduzido através de um modelo de sistema MIMO convencional, em que são considerados canais com estimativas imperfeitas e com correlação entre as antenas transmissoras (Tx) e entre as receptoras (Rx). Aplicam-se técnicas de pré-processamanto baseadas na redução treliça (LR - lattice reduction) em detectores lineares, além do detector esférico (SD - sphere decoder), o qual é proposto um procedimento de tabela de pesquisa a fim de prover redução na complexidade computacional. Mostra-se que o método LR na pré-detecção resulta em ganho de desempenho significante tanto na condição de canais descorrelacionados quanto fortemente correlacionados, sendo que, neste último cenário a melhoria é ainda mais notável, devido ao ganho de diversidade proporcionado. Por outro lado, a complexidade envolvida na aplicação da LR em alta correlação torna-se preponderante em detectores lineares. No LR-SD utilizando o procedimento de tabela de pesquisa, o ganho ótimo foi alcançado em todos os cenários, como esperado, e resultou em complexidade inferior ao detector de máxima verossimilhança (ML - maximum likelihood), mesmo com máxima correlação entre antenas, a qual representa o cenário de maior complexidade a técnica LR. Em seguida, o detector por troca de mensagens (MP - message passing) é investigado, o qual faz uso de modelos grafos do tipo MRF (Markov random fields) e FG (factor graph). Além disso, mostra-se na literatura que o método de amortecimento de mensagens (MD - message damping) aplicado ao detector MRF traz relevante ganho de desempenho sem aumento na complexidade computacional. Por outro lado, o valor do DF (damping factor) é especificado para somente uma variedade restrita de cenários. Resultados numéricos são extensivamente gerados, de forma a dispor de uma gama de análises de comportamento do MRF com MD, resultando na proposição de um valor ótimo para o DF, baseando-se em ajuste de curva numérico. Finalmente, em face ao detector MGS (mixed Gibbs sampling), são propostas duas abordagens visando a redução do impacto negativo causado pela solução aleatória quando altas ordens de modulação são empregadas. A primeira é baseada em uma média entre múltiplas amostras, chamada aMGS (averaged MGS). A segunda abordagem realiza uma restrição direta no alcance da solução aleatória, limitando em até d a vizinhança de símbolos que podem ser sorteados, sendo chamada de d-sMGS (d-simplificado MGS). Resultados de simulação numérica demonstram que ambas abordagens resultam em ganho de convergência em relação ao MGS, destacando-se: em regiões de alto carregamento, a detecção d-sMGS demonstrou ganho expressivo tanto em desempenho quanto em complexidade se comparada à aMGS e MGS; já em baixo-médio carregamentos, a estratégia aMGS demonstrou menor complexidade, com desempenho marginalmente semelhante às demais. Além disso, conclui-se que o aumento do número de dimensões do sistema favorece uma menor restrição na vizinhança.

Due to the highly-varying wireless channels over time, frequency, and space domains, statistical QoS provisioning, instead of deterministic QoS guarantees, has become a recognized feature in the next-generation wireless networks. In this dissertation, we study the adaptive wireless resource allocation problems for statistical QoS provisioning, such as guaranteeing the specified delay-bound violation probability, upper-bounding the average loss-rate, optimizing the average goodput/throughput, etc., in several typical types of mobile wireless networks. In the first part of this dissertation, we study the statistical QoS provisioning for mobile multicast through the adaptive resource allocations, where different multicast receivers attempt to receive the common messages from a single base-station sender over broadcast fading channels. Because of the heterogeneous fading across different multicast receivers, both instantaneously and statistically, how to design the efficient adaptive rate control and resource allocation for wireless multicast is a widely cited open problem. We first study the time-sharing based goodput-optimization problem for non-realtime multicast services. Then, to more comprehensively characterize the QoS provisioning problems for mobile multicast with diverse QoS requirements, we further integrate the statistical delay-QoS control techniques — effective capacity theory, statistical loss-rate control, and information theory to propose a QoS-driven optimization framework. Applying this framework and solving for the corresponding optimization problem, we identify the optimal tradeoff among statistical delay-QoS requirements, sustainable traffic load, and the average loss rate through the adaptive resource allocations and queue management. Furthermore, we study the adaptive resource allocation problems for multi-layer video multicast to satisfy diverse statistical delay and loss QoS requirements over different video layers. In addition, we derive the efficient adaptive erasure-correction coding scheme for the packet-level multicast, where the erasure-correction code is dynamically constructed based on multicast receivers’ packet-loss statuses, to achieve high error-control efficiency in mobile multicast networks. In the second part of this dissertation, we design the adaptive resource allocation schemes for QoS provisioning in unicast based wireless networks, with emphasis on statistical delay-QoS guarantees. First, we develop the QoS-driven time-slot and power allocation schemes for multi-user downlink transmissions (with independent messages) in cellular networks to maximize the delay-QoS-constrained sum system throughput. Second, we propose the delay-QoS-aware base-station selection schemes in distributed multiple-input-multiple-output systems. Third, we study the queueaware spectrum sensing in cognitive radio networks for statistical delay-QoS provisioning. Analyses and simulations are presented to show the advantages of our proposed schemes and the impact of delay-QoS requirements on adaptive resource allocations in various environments.

La radio impulsionnelle UWB (IR-UWB) est une technologie de communication relativement récente, qui apporte une solution intéressante au problème de l’encombrement du spectre RF, et qui répond aux exigences de haut débit et localisation précise d’un nombre croissant d’applications, telles que les communications indoor, les réseaux de capteurs personnels et corporels, l’IoT, etc. Ses caractéristiques uniques sont obtenues par la transmission d’impulsions de très courte durée (inférieure à 1 ns), occupant une largeur de bande allant jusqu’à 7,5 GHz, et ayant une densité spectrale de puissance extrêmement faible (inférieure à -43 dBm/MHz). Les meilleures performances d’un système IR-UWB sont obtenues avec des récepteurs cohérents de type Rake, au prix d’une complexité accrue, due notamment à l’étape d’estimation du canal UWB, caractérisé par de nombreux trajets multiples. Cette étape de traitement nécessite l’estimation d’un ensemble de composantes spectrales du signal reçu, sans pouvoir faire appel aux techniques d’échantillonnage usuelles, en raison d’une limite de Nyquist particulièrement élevée (plusieurs GHz).Dans le cadre de cette thèse, nous proposons de nouvelles approches, à faible complexité, pour l’estimation du canal UWB, basées sur la représentation parcimonieuse du signal reçu, la théorie de l’acquisition compressée, et les méthodes de reconstruction des signaux à taux d’innovation fini. La réduction de complexité ainsi obtenue permet de diminuer de manière significative le coût d’implémentation du récepteur IR-UWB et sa consommation. D’abord, deux schémas d’échantillonnage compressé, monovoie (filtre SoS) et multivoie (MCMW) identifiés dans la littérature sont étendus au cas des signaux UWB ayant un spectre de type passe-bande, en tenant compte de leur implémentation réelle dans le circuit. Ces schémas permettent l’acquisition des coefficients spectraux du signal reçu et l’échantillonnage à des fréquences très réduites ne dépendant pas de la bande passante des signaux, mais seulement du nombre des trajets multiples du canal UWB. L’efficacité des approches proposées est démontrée au travers de deux applications : l’estimation du canal UWB pour un récepteur Rake cohérent à faible complexité, et la localisation précise en environnement intérieur dans un contexte d’aide à la dépendance.En outre, afin de réduire la complexité de l’approche multivoie en termes de nombre de voies nécessaires pour l’estimation du canal UWB, nous proposons une architecture à nombre de voies réduit, en augmentant le nombre d’impulsions pilotes émises.Cette même approche permet aussi la réduction de la fréquence d’échantillonnage associée au schéma MCMW. Un autre objectif important de la thèse est constitué par l’optimisation des performances des approches proposées. Ainsi, bien que l’acquisition des coefficients spectraux consécutifs permette une mise en oeuvre simple des schémas multivoie, nous montrons que les coefficients ainsi choisis, ne donnent pas les performances optimales des algorithmes de reconstruction. Ainsi, nous proposons une méthode basée sur la cohérence des matrices de mesure qui permet de trouver l’ensemble optimal des coefficients spectraux, ainsi qu’un ensemble sous-optimal contraint où les positions des coefficients spectraux sont structurées de façon à faciliter la conception du schéma MCMW. Enfin, les approches proposées dans le cadre de cette thèse sont validées expérimentalement à l’aide d’une plateforme expérimentale UWB du laboratoire Lab-STICC CNRS UMR 6285. / Ultra-wideband impulse radio (IR-UWB) is a relatively new communication technology that provides an interesting solution to the problem of RF spectrum scarcity and meets the high data rate and precise localization requirements of an increasing number of applications, such as indoor communications, personal and body sensor networks, IoT, etc. Its unique characteristics are obtained by transmitting pulses of very short duration (less than 1 ns), occupying a bandwidth up to 7.5 GHz, and having an extremely low power spectral density (less than -43 dBm / MHz). The best performances of an IR-UWB system are obtained with Rake coherent receivers, at the expense of increased complexity, mainly due to the estimation of UWB channel, which is characterized by a large number of multipath components. This processing step requires the estimation of a set of spectral components for the received signal, without being able to adopt usual sampling techniques, because of the extremely high Nyquist limit (several GHz).In this thesis, we propose new low-complexity approaches for the UWB channel estimation, relying on the sparse representation of the received signal, the compressed sampling theory, and the reconstruction of the signals with finite rate of innovation. The complexity reduction thus obtained makes it possible to significantly reduce the IR-UWB receiver cost and consumption. First, two existent compressed sampling schemes, single-channel (SoS) and multi-channel (MCMW), are extended to the case of UWB signals having a bandpass spectrum, by taking into account realistic implementation constraints. These schemes allow the acquisition of the spectral coefficients of the received signal at very low sampling frequencies, which are not related anymore to the signal bandwidth, but only to the number of UWB channel multipath components. The efficiency of the proposed approaches is demonstrated through two applications: UWB channel estimation for low complexity coherent Rake receivers, and precise indoor localization for personal assistance and home care.Furthermore, in order to reduce the complexity of the MCMW approach in terms of the number of channels required for UWB channel estimation, we propose a reduced number of channel architecture by increasing the number of transmitted pilot pulses. The same approach is proven to be also useful for reducing the sampling frequency associated to the MCMW scheme.Another important objective of this thesis is the performance optimization for the proposed approaches. Although the acquisition of consecutive spectral coefficients allows a simple implementation of the MCMW scheme, we demonstrate that it not results in the best performance of the reconstruction algorithms. We then propose to rely on the coherence of the measurement matrix to find the optimal set of spectral coefficients maximizing the signal reconstruction performance, as well as a constrained suboptimal set, where the positions of the spectral coefficients are structured so as to facilitate the design of the MCMW scheme. Finally, the approaches proposed in this thesis are experimentally validated using the UWB equipment of Lab-STICC CNRS UMR 6285.

Space-Time Block Coding is an efficient communication technique used in multiple-input multiple-output wireless systems. The complexity with which a Space-Time Block Code (STBC) can be decoded is important from an implementation point of view since it directly affects the receiver complexity and speed. In this thesis, we address the problem of designing low complexity decoding techniques for STBCs, and constructing STBCs that achieve high rate and full-diversity with these decoders. This thesis is divided into two parts; the first is concerned with the optimal decoder, viz. the maximum-likelihood (ML) decoder, and the second with non-ML decoders. An STBC is said to be multigroup ML decodable if the information symbols encoded by it can be partitioned into several groups such that each symbol group can be ML decoded independently of the others, and thereby admitting low complexity ML decoding. In this thesis, we first give a new framework for constructing low ML decoding complexity STBCs using codes over the Klein group, and show that almost all known low ML decoding complexity STBCs can be obtained by this method. Using this framework we then construct new full-diversity STBCs that have the least known ML decoding complexity for a large set of choices of number of transmit antennas and rate. We then introduce the notion of Asymptotically-Good (AG) multigroup ML decodable codes, which are families of multigroup ML decodable codes whose rate increases linearly with the number of transmit antennas. We give constructions for full-diversity AG multigroup ML decodable codes for each number of groups g > 1. For g > 2, these are the first instances of g-group ML decodable codes that are AG or have rate more than 1. For g = 2 and identical delay, the new codes match the known families of AG codes in terms of rate. In the final section of the first part we show that the upper triangular matrix R encountered during the sphere-decoding of STBCs can be rank-deficient, thus leading to higher sphere-decoding complexity, even when the rate is less than the minimum of the number of transmit antennas and the number receive antennas. We show that all known AG multigroup ML decodable codes suffer from such rank-deficiency, and we explicitly derive the sphere-decoding complexities of most known AG multigroup ML decodable codes. In the second part of this thesis we first study a low complexity non-ML decoder introduced by Guo and Xia called Partial Interference Cancellation (PIC) decoder. We give a new full-diversity criterion for PIC decoding of STBCs which is equivalent to the criterion of Guo and Xia, and is easier to check. We then show that Distributed STBCs (DSTBCs) used in wireless relay networks can be full-diversity PIC decoded, and we give a full-diversity criterion for the same. We then construct full-diversity PIC decodable STBCs and DSTBCs which give higher rate and better error performance than known multigroup ML decodable codes for similar decoding complexity, and which include other known full-diversity PIC decodable codes as special cases. Finally, inspired by a low complexity essentially-ML decoder given by Sirianunpiboon et al. for the two and three antenna Perfect codes, we introduce a new non-ML decoder called Adaptive Conditional Zero-Forcing (ACZF) decoder which includes the technique of Sirianunpiboon et al. as a special case. We give a full-diversity criterion for ACZF decoding, and show that the Perfect codes for two, three and four antennas, the Threaded Algebraic Space-Time code, and the 4 antenna rate 2 code of Srinath and Rajan satisfy this criterion. Simulation results show that the proposed decoder performs identical to ML decoding for these five codes. These STBCs along with ACZF decoding have the best error performance with least complexity among all known STBCs for four or less transmit antennas.

Image compression is of great importance in multimedia systems and applications because it drastically reduces bandwidth requirements for transmission and memory requirements for storage. Although earlier standards for image compression were based on the Discrete Cosine Transform (DCT), a recently developed mathematical technique, called Discrete Wavelet Transform (DWT), has been found to be more efficient for image coding. Despite improvements in compression efficiency, wavelet image coders significantly increase memory usage and complexity when compared with DCT-based coders. A major reason for the high memory requirements is that the usual algorithm to compute the wavelet transform requires the entire image to be in memory. Although some proposals reduce the memory usage, they present problems that hinder their implementation. In addition, some wavelet image coders, like SPIHT (which has become a benchmark for wavelet coding), always need to hold the entire image in memory. Regarding the complexity of the coders, SPIHT can be considered quite complex because it performs bit-plane coding with multiple image scans. The wavelet-based JPEG 2000 standard is still more complex because it improves coding efficiency through time-consuming methods, such as an iterative optimization algorithm based on the Lagrange multiplier method, and high-order context modeling. In this thesis, we aim to reduce memory usage and complexity in wavelet-based image coding, while preserving compression efficiency. To this end, a run-length encoder and a tree-based wavelet encoder are proposed. In addition, a new algorithm to efficiently compute the wavelet transform is presented. This algorithm achieves low memory consumption using line-by-line processing, and it employs recursion to automatically place the order in which the wavelet transform is computed, solving some synchronization problems that have not been tackled by previous proposals. The proposed encode / Oliver Gil, JS. (2006). On the design of fast and efficient wavelet image coders with reduced memory usage [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1826