Visible light communication (VLC) systems take advantage of ubiquitous light-emitting diodes (LED) and leverage existing illumination infrastructure to provide broadband optical communication links. Multiple-input multiple-output (MIMO) VLC systems are among the well studied topics in VLC research. However, most traditional MIMO VLC systems require accurate alignment and have to adjust to different magnifications at various link distances. Consequently, the alignment and calibration modules increase the complexity of the receiver structures. A pixelated MIMO VLC system is introduced in this thesis, which transmits a series of time-varying coded images that can be received and decoded by commercial digital cameras. Using a convex lens placed in front of the transmitter at its focal length, the system exploits the Bokeh effect to obtain fixed-scale images at all link distances. Compared with traditional pixelated MIMO VLC systems, which send information directly in space, this spatial-angular mapping system sends information in different angles instead. In contrast to the complex receiver structures in traditional setups, the proposed system can capture fixed-scale images with a simple receiver requiring no re-focusing as the camera moves. The channel model of the system is measured and modeled and a rateless code is applied to track the truncation of receive images for various link ranges and angular offsets. A proof-of-concept optical communication system is implemented with an LCD display and a high speed CMOS camera. Performance of the system is measured and analysed. The experimental system can achieve a throughput of approximately 10 bit per frame over 90 cm. This fixed-scale pixelated MIMO wireless optical communication system provides a less expensive option for short-range indoor broadcasting optical links and inter-vehicle communications due to its mobility, stability and simpler receiver structure compared to traditional designs in different working conditions. / Thesis / Master of Applied Science (MASc)

Yes / In this work, a compact 4-element multiple-input multiple-output (MIMO) antenna system is presented for sub-6 GHz applications. A modified M-shaped strip is used to form each antenna element in the MIMO system. To improve performance, a rectangular-shaped area is etched on the opposite side of each element in the ground plane. The antenna size is 100 × 60 mm2. Most interestingly, the port isolation is improved by rotating the etched areas and the corresponding radiating elements. This one-of-a-kind approach aided in the development of a highly isolated MIMO antenna with a small footprint. The theory of characteristic modes (TCM) is used to analyze the behavior of rotating the etched areas in the ground of the antenna. The antenna provides significant port isolation above 20 dB, stable radiation patterns, and an outstanding ECC of less than 0.01. The design is simple and compact, making it suitable for MIMO operation on handheld devices.

This research addresses green communication issues, including energy efficiency, peak-to-average power ratio (PAPR) reduction and power amplifier (PA) linearization. Green communication is expected to be a primary goal in next generation cellular systems because it promises to reduce operating costs. The first key issue is energy efficiency of distributed antenna systems (DASs). The power consumption of high power amplifiers (HPAs) used in wireless communication systems is determined by the transmit power and drain efficiency. For unequal power allocation of orthogonal frequency division multiplexing (OFDM), the drain efficiency of the PA is determined by the PAPR and hence by the power distribution. This research proposes a PAPR-aware energy-efficient resource allocation scheme for joint orthogonal frequency division multiple access (OFDMA)/space division multiple access (SDMA) downlink transmission from DASs. Grouping-based SDMA is applied to exploit the spatial diversity while avoiding performance degradation from correlated channels. The developed scheme considers the impact of both system data rate and effective power consumption on the PAPR during resource allocation. We also present a suboptimal joint subcarrier and power allocation algorithm to facilitate implementation of power-efficient multi-channel wireless communications. By solving Karush-Kuhn-Tucker conditions, a closed-form solution for the power allocation of each remote radio head is obtained. The second key issue is related with PAPR reduction in the massive multiple-input multiple-output (MIMO) systems. The large number of PAs in next generation massive MIMO cellular communication system requires using inexpensive PAs at the base station to keep array cost reasonable. Large-scale multiuser (MU) MIMO systems can provide extra spatial degrees-of-freedom (DoFs) for PAPR reduction. This work applies both recurrent neural network (RNN)- and semidefinite relaxation (SDR)-based schemes for different purposes to reduce PAPR. The highly parallel structure of RNN is proposed in this work to address the issues of scalability and stringent requirements on computational times in PAPR-aware precoding problem. An SDR-based framework is proposed to reduce PAPR that accommodates channel uncertainties and intercell coordination. Both of the proposed structures reduce linearity requirements and enable the use of lower cost RF components for large-scale MU-MIMO-OFDM downlink. The third key issue is digital predistortion (DPD) in the massive MIMO systems. The primary source of nonlinear distortion in wireless transmitters is the PA, which is commonly modeled using polynomials. Conventional DPD schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is impractical for scaling to tens or hundreds of PAs in massive MIMO systems. This work therefore proposes a scalable DPD method, achieved by exploiting massive DoFs of next generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero-forcing precoding and DPD. Experimental results show that over 70% of computational complexity is saved for the proposed solution, it is shown that a 3rd order polynomial with the new solution achieves the same performance as the conventional DPD using 11th order polynomial for a 100x10 massive MIMO configuration. / Ph. D.

yes / A compact dual U-shaped slot PIFA antenna with Electromagnetic Bandgap (EBG) material on a relatively low dielectric constant substrate is presented. Periodic structures have found to reduce mutual coupling and decrease the separation of antenna and ground plane. A design with EGB material suitable for a small terminal mobile handset operating at 2.4 GHz was studied. Simulated and measured scattering parameters are compared for U-shaped slot PIFA antenna with and without EBG structures. An evaluation of MIMO antennas is presented, with analysis of the mutual coupling, correlation coefficient, total active reflection coefficient (TARC), channel capacity and capacity loss. The proposed antenna meets the requirements for practical application within a mobile handset. / Electronics and Telecommunications

Environmental testing is essential to certify systems to withstand the harsh dynamic loads they may experience in their service environment or during transport. For example, satel- lites are subjected to large vibration and acoustic loads when transported into orbit and need to be certified with tests that are representative of the anticipated loads. However, tra- ditional certification testing specifications can consist of sequential uniaxial vibration tests, which have been found to severely over- and under-test systems needing certification. The recently developed Impedance-Matched Multi-Axis Test (IMMAT) has been shown in the literature to improve upon traditional environmental testing practices through the use of multi-input multi-output testing and impedance matching. Additionally, with the use of numerical models, predictive simulations can be performed to determine optimal testing pa- rameters. Developing an accurate numerical model, however, requires precise knowledge of the system's dynamic characteristics, such as boundary conditions or material properties. These characteristics are not always available and would also require additional testing for verification. Furthermore, some systems may be extremely difficult to model using numerical methods because they contain millions of finite elements requiring impractical times scales to simulate or because they were fabricated before mainstream use of computer aided drafting and finite element analysis but are still in service. An alternative to numerical modeling is data-driven modeling, which does not require knowledge of a system's dynamic characteris- tics. The Continuous Residue Interpolation (CRI) method has been recently developed as a novel approach for building data-driven models of dynamical systems. CRI builds data- driven models by fitting smooth, continuous basis functions to a subset of frequency response function (FRF) measurements from a dynamical system. The resulting fitted basis functions can be sampled at any geometric location to approximate the expected FRF at that location. The research presented in this thesis explores the use of CRI-derived data-driven models in predictive simulations for the IMMAT performed on a Euler-Bernoulli beam. The results of the simulations reveal that CRI-derived data-driven models of a Euler-Bernoulli beam achieve similar performance when compared to a finite element model and make similar decisions when deciding the excitation locations in an IMMAT. / Master of Science / In the field of vibrations testing, environmental tests are used to ensure that critical devices or structures can withstand harsh vibration environments. For example, satellites experience harsh vibrations and damaging acoustics that are transferred from it's rocket transport vehicle. Traditional environmental tests would require that the satellite be placed on a vibration table and sequentially vibrated in multiple orientations for a specified duration and intensity. However, these traditional environmental tests do not always produce vibrations that are representative of the anticipated transport or operational environment. Newly developed methods, such as the Impedance-Matched Multi-Axis Test (IMMAT) methods achieves representative test results by matching the mounting characteristics of the structure during it's transport or operational environment and vibrating the structure in multiple directions simultaneously. An IMMAT can also be optimized by using finite element models (FEM), which approximate the device to be tested with a discrete number of small volumes whose physics are described by fundamental equations of motion. However, an FEM can only be used if it's dynamic characteristics are sufficiently similar to the structure undergoing testing. This can only be achieved with precise knowledge of the dynamical properties of the structure, which is not always available. An alternate approach to an FEM is to use a data-driven model. Because data-driven models are made using data from the system it is supposed to describe, dynamical properties of the device are pre-built in the model and is not necessary to approximate them. Continuous Residue Interpolation (CRI) is a recently developed data-driven modeling scheme that approximates a structure's dynamic properties with smooth, continuous functions updated with measurements of the input-output response dynamics of the device. This thesis presents the performance of data-driven models generated using CRI when used in predictive simulations of an IMMAT. The results show that CRI- derived data-driven models perform similarly to FEMs and make similar predictions for optimal input vibration locations.

No

The next generation of mobile networks (5G) is currently being standardized, and massive MIMO (Multiple-Input-Multiple-Output) is a strong candidate to be part of this standard. Other than providing higher data rates and lower latency, high accuracy positioning is also required. In this thesis, we evaluate the achievable performance of positioning using massive MIMO systems in open highway scenarios. Relevant theory from sensor array signal processing and Bayesian filtering is presented, and is used in a simulation environment on large antenna arrays representing massive MIMO base stations. Positioning is done by utilizing the uplink pilot reference signals, where the Direction of Arrival (DOA) of the pilot signal is estimated, and then used for position estimation. Estimation of the DOA is done by both a maximum-likelihood method and by using an Extended Kalman Filter (EKF). A positioning error of less than 8 m is achieved with absolute certainty when the vehicle is less than 300 m from the base station. It is also concluded that this result could be improved by using more sophisticated filtering algorithms.

La thèse aborde le problème de la détection d’un signal dans une bande de fréquences donnée sans aucune connaissance à priori sur la source (détection aveugle) dans le contexte de la radio intelligente. Le détecteur proposé dans la thèse est basé sur l’estimation des valeurs propres de la matrice de corrélation du signal reçu. A partir de ces valeurs propres, plusieurs critères ont été développés théoriquement (Standard Condition Number, Scaled Largest Eigenvalue, Largest Eigenvalue) en prenant pour hypothèse majeure un nombre fini d’éléments, contrairement aux hypothèses courantes de la théorie des matrices aléatoires qui considère un comportement asymptotique de ces critères. Les paramètres clés des détecteurs ont été formulés mathématiquement (probabilité de fausse alarme, densité de probabilité) et une correspondance avec la densité GEV a été explicitée. Enfin, ce travail a été étendu au cas multi-antennes (MIMO) pour les détecteurs SLE et SCN. / In Cognitive Radio, Spectrum Sensing (SS) is the task of obtaining awareness about the spectrum usage. Mainly it concerns two scenarios of detection: (i) detecting the absence of the Primary User (PU) in a licensed spectrum in order to use it and (ii) detecting the presence of the PU to avoid interference. Several SS techniques were proposed in the literature. Among these, Eigenvalue Based Detector (EBD) has been proposed as a precious totally-blind detector that exploits the spacial diversity, overcome noise uncertainty challenges and performs adequately even in low SNR conditions. The first part of this study concerns the Standard Condition Number (SCN) detector and the Scaled Largest Eigenvalue (SLE) detector. We derived exact expressions for the Probability Density Function (PDF) and the Cumulative Distribution Function (CDF) of the SCN using results from finite Random Matrix Theory; In addition, we derived exact expressions for the moments of the SCN and we proposed a new approximation based on the Generalized Extreme Value (GEV) distribution. Moreover, using results from the asymptotic RMT we further provided a simple forms for the central moments of the SCN and we end up with a simple and accurate expression for the CDF, PDF, Probability of False-Alarm, Probability of Detection, of Miss-Detection and the decision threshold that could be computed and hence provide a dynamic SCN detector that could dynamically change the threshold value depending on target performance and environmental conditions. The second part of this study concerns the massive MIMO technology and how to exploit the large number of antennas for SS and CRs. Two antenna exploitation scenarios are studied: (i) Full antenna exploitation and (ii) Partial antenna exploitation in which we have two options: (i) Fixed use or (ii) Dynamic use of the antennas. We considered the Largest Eigenvalue (LE) detector if noise power is perfectly known and the SCN and SLE detectors when noise uncertainty exists.

Le relayage cognitif multiple-input multiple-output (MIMO) hérite l’efficacité spectrale de la radiocognitive et les systèmes de relayage MIMO, apportant ainsi des gains prometteurs en termes de débit dedonnées et de fiabilité pour les futures communications sans fil et mobiles. Dans cette thèse, nous concevons et évaluons des schémas pratiques d’émetteurs et de récepteurs pour des systèmes de relayage MIMO cognitifs qui peuvent être mis en oeuvre à moindre coût. Tout d'abord, nous réduisons l'affaiblissement du débit du mode half-duplex du relayage MIMO amplify-and-forward non-orthogonale(NAF) large bande avec demande de répétition automatique (ARQ). Différemment des travaux de recherche existants, le protocole de relayage proposé ne nécessite que la durée de transmission d’un seul paquet sur des canaux sélectifs en fréquence. De plus, nous proposons une conception de réception itérative à complexité réduite pour cette classe de protocoles, entraînant ainsi une amélioration significative des performances de transmission de bout-en-bout. Deuxièmement, nous nous concentrons sur les systèmes de relayage cognitive de partage du spectre single-input multiple-output (SIMO) et évaluons l’impact des contraintes d’interférence instantanée et statistique sur la qualité de leur probabilité de coupure. Nos résultats révèlent que l’imposition d’une contrainte statistique sur la puissance d’émission du système secondaire est plus favorable que son adversaire consommatrice de spectre. Troisièmement, nous capitalisons sur notre deuxième contribution pour étudier les systèmes de relayage MIMO decode-and-forward (DF) cognitifs utilisant la sélection d'antenne à l’émission (TAS) ainsi que le maximum-ratio combining (MRC) à la réception. Basés sur la maximisation du rapport signal-sur-bruit (SNR) ou du rapport signal-sur-interférence-plus-bruit (SINR), nos résultats de probabilité de coupure nouvellement dérivés pour les deux stratégies proposées de TAS démontre l’optimalité du système de sélection d’antenne basé sur le SINR par rapport aux effets néfastes d’interférence mutuelle dans les systèmes de relayage MIMO DF cognitifs. / Cognitive multiple-input multiple-output (MIMO) relaying inherits the spectrum usage efficiency from both cognitive radio and MIMO relay systems, thereby bearing promising gains in terms of data rate and reliability for future wireless and mobile communications. In this dissertation, we design and evaluate practical transmitter and receiver schemes for cognitive MIMO relay systems that can readily be implemented at a lower cost. First, we reduce the multiplexing loss due the half-duplex operation in non orthogonal amplify-and-forward (NAF) MIMO relay broadband transmissions with automatic repeat request(ARQ). Different from existing research works, the proposed relaying protocol requires only one packet duration to operate over frequency-selective block-fading relay channels. Further, we propose a low complexityiterative receiver design for this class of protocols which results in significant enhancement of the end-to-end transmission performance. Second, we focus on cognitive underlay single-input multiple-output (SIMO) relay systems and evaluate the impact of instantaneous and statistical interference constraints on their outage performance. Our results reveal that imposing a statistical interference constraint on the secondary system transmit power is most favored than its spectrum-consuming counter part. Third, we capitalize on our second contribution to investigate cost-effective transmission schemes for cognitive MIMO decode-and-forward (DF) relaying systems employing transmit-antenna selection (TAS) along with maximum-ratio combining (MRC) at the transmitter and receiver sides, respectively. Driven by maximizing either the received signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR), our newly derived outage performance results pertaining to both proposed TAS strategies are shown to entail an involved derivation roadmap yet demonstrate the optimality of the SINR-driven TAS against the detrimental effect of mutual interference incognitive MIMO DF relay systems.

La thèse s'inscrit dans la perspective de l'explosion du trafic de données générée par l'augmentation du nombre d'utilisateurs ainsi que la croissance du débit qui doivent être prises en compte dans la définition des futures générations de communications radiocellulaires. Une solution est la technologie «large-scale MIMO » (systèmes MIMO de grande dimension) qui pose plusieurs défis. La conception des nouveaux algorithmes de détection de faible complexité est indispensable vu que les algorithmes classiques ne sont plus adaptés à cette configuration à cause de leurs mauvaises performances de détection ou de leur complexité trop élevée fonction du nombre d'antennes. Une première contribution de la thèse est un algorithme basé sur la technique de l'acquisition comprimée en exploitant les propriétés des signaux à alphabet fini. Appliqué à des systèmes MIMO de grande dimension, déterminés et sous-déterminés, cet algorithme réalise des performances (qualité de détection, complexité) prometteuses et supérieures comparé aux algorithmes de l'état de l'art. Une étude théorique approfondie a été menée pour déterminer les conditions optimales de fonctionnement et la distribution statistique des sorties. Une seconde contribution est l'intégration de l'algorithme original dans un récepteur itératif en différenciant les cas codé (code correcteur d'erreurs présent) et non codé. Un autre défi pour tenir les promesses des systèmes large scale MIMO (efficacité spectrale élevée) est l'estimation de canal. Une troisième contribution de la thèse est la proposition d'algorithmes d'estimation semi-aveugles qui fonctionnent avec une taille minimale des séquences d'apprentissage (égale au nombre d'utilisateurs) et atteignent des performances très proches de la borne théorique. / The thesis is part of the prospect of the explosion of data traffic generated by the increase of the number of users as well as the growth of the bit rate which must be taken into account in the definition of future generations of radio-cellular communications. A solution is the large-scale MIMO technology (MIMO systems oflarge size) which poses several challenges. The design of the new low complexity detection algorithms is indispensable since the conventional algorithms are no longer adapted to this configuration because of their poor detection performance or their too high complexity depending on the number of antennas. A first contribution of the thesis is an algorithm based on the technique of compressed sensing by exploiting the propertiesof the signals with finite alphabet. Applied to large-scale, determined and under-determined MIMO systems, this algorithm achieves promising and superior performance (quality ofdetection, complexity) compared to state-ofthe-art algorithms. A thorough theoretical study was conducted to determine the optimal operating conditions and the statistical distribution of outputs. A second contribution is the integration of the original algorithm into an iterative receiver by differentiating the coded and uncoded cases. Another challenge to keeping the promise of large- scale MIMO systems (high spectral efficiency) is channel estimation. A third contribution of the thesis is the proposal of semi-blind channel estimation algorithms that work with a minimum size of pilot sequences (equal to the number of users) and reach performances very close to the theoretical bound.