[pt] O emprego conjunto de Redes de Acesso por Rádio em Nuvem (CRANs) e sistemas de múltiplas entradas e múltiplas saídas (MIMO) de larga escala é uma solução chave para atender aos requisitos da quinta geração (5G) de redes sem fio. No entanto, alguns desafios ainda precisam ser superados como a redução do consumo de energia do sistema, a capacidade limitada dos links fronthaul e a redução dos custos de implantação e operação. Embora seja prejudicial para o desempenho do sistema, a quantização em baixa resolução é proposta como uma solução para estes desafios. Portanto, técnicas que melhoram o desempenho de sistemas quantizados grosseiramente são necessárias. Em sistemas móveis, os ADCs geralmente são precedidos por um controle de ganho automático (AGC). O AGC trabalha moldando a amplitude do sinal recebido dentro do intervalo do quantizador para usar eficientemente a resolução. A fim de solucionar esses problemas, esta dissertação apresenta uma otimização conjunta do AGC, que funciona nas cabeças de rádio remotas (RRHs), e um filtro de recepção linear de baixa resolução consciente (LRA) baseado no mínimo erro quadrático médio (MMSE), que funciona na unidade de nuvem (CU), para sistemas quantizados grosseiramente. Desenvolvemos receptores de cancelamento de interferência lineares e sucessivos (SIC) com base na proposta conjunta de AGC e LRA MMSE (AGC-LRA-MMSE). Uma análise da soma das taxas alcançáveis juntamente com um estudo de complexidade computacional também são realizadas. As simulações mostram que o projeto proposto fornece taxas de erro reduzidas e taxas alcançáveis mais altas do que as técnicas existentes. / [en] The joint employment of Cloud Radio Access Networks (C-RANs) and large-scale multiple-input multiple-output (MIMO) systems is a key solution to fulfill the requirements of the fifth generation (5G) of wireless networks. However, some challenges are still open to be overcome such as the high power consumption of large-scale MIMO systems, which employ a large number of analog-to-digital converters (ADCs), the capacity bottleneck of the fronthaul links and the system cost reduction. Although it often affects the system performance, the low-resolution quantization is a possible solution for these problems. Therefore, techniques that improve the performance of coarsely quantized systems are needed. In mobile applications, the ADCs are usually preceded by an automatic gain control (AGC). The AGC works shaping the received signal amplitude within the quantizer range to efficiently use the ADC resolution. Then, the optimization of an AGC is especially important. In order to present possible solutions for these issues, this thesis presents a joint optimization of the AGC, which works in the remote radio heads (RRHs), and a low-resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works in the cloud unit (CU), for coarsely quantized large-scale MIMO with CRAN systems. We develop linear and successive interference cancellation (SIC) receivers based on the proposed joint AGC and LRA MMSE (AGCLRAMMSE) approach. An analysis of the achievable sum rates along with a computational complexity study is also carried out. Simulations show that the proposed design provides improved error rates and higher achievable rates than existing techniques.

<p>The Multiple-input and Multiple-output (MIMO) channel model is very useful for the presentation of a wide range of wireless communication systems. This thesis addresses the joint design of a precoder and a receiver for a MIMO channel model, in a scenario in which perfect channel state information (CSI) is available at both ends. We develop a novel framework for the transmitting-receiving procedure. Under the proposed framework, the receiver decomposes the channel matrix by using a block QR decomposition, where Q is a unitary matrix and R is a block upper triangular matrix. The optimal maximum likelihood (ML) detection process is employed within each diagonal block of R. Then, the detected block of symbols is substituted and subtracted sequentially according to the block QR decomposition based successive cancellation. On the transmitting end, the expression of probability of error based on ML detection is chosen as the design criterion to formulate the precoder design problem. This thesis presents a design of MIMO transceivers in the particular case of having 4 transmitting and 4 receiving antennas with full CSI knowledge on both sides. In addition, a closed-form expression for the optimal precoder matrix is obtained for channels satisfying certain conditions. For other channels not satisfying the specific condition, a numerical method is applied to obtain the optimal precoder matrix.</p> / Master of Applied Science (MASc)

The research reported in this thesis is concerned with robust precoder and transceiver optimization in multiuser multi-antenna wireless communication systems in the presence of imperfect channel state information(CSI). Precoding at the transmit side, which utilizes the CSI, can improve the system performance and simplify the receiver design. Transmit precoding is essential for inter-user interference cancellation in multiuser downlink where users do not cooperate. Linear and non-linear precoding have been widely investigated as low-complexity alternatives to dirty paper coding-based transmission scheme for multiuser multiple-input multiple-output(MU-MIMO)downlink. Similarly, in relay-assisted networks, precoding at the relay nodes have been shown to improve performance. The precoder and joint precoder/receive filter (transceiver) designs usually assume perfect knowledge of the CSI. In practical systems, however, the CSI will be imperfect due to estimation errors, feedback errors and feedback delays. Such imperfections in CSI will lead to deterioration of performance of the precoders/transceivers designed assuming perfect CSI. In such situations, designs which are robust to CSI errors are crucial to realize the potential of multiuser multi-antenna systems in practice. This thesis focuses on the robust designs of precoders and transceivers for MU-MIMO downlink, and for non-regenerative relay networks in the presence of errors in the CSI. We consider a norm-bounded error(NBE) model, and a stochastic error(SE) model for the CSI errors. These models are suitable for commonly encountered errors, and they allow mathematically and computationally tractable formulations for the robust designs. We adopt a statistically robust design in the case of stochastic error, and a minimax or worst-case robust design in the case of norm-bounded error. We have considered the robust precoder and transceiver designs under different performance criteria based on transmit power and quality-of-service(QoS) constraints. The work reported in this thesis can be grouped into three parts, namely,i ) robust linear pre-coder and transceiver designs for multiuser downlink, ii)robust non-linear precoder and transceiver designs for multiuser downlink, and iii)robust precoder designs for non-regenerative relay networks. Linear precoding: In this part, first, a robust precoder for multiuser multiple-input single-output(MU-MISO)downlink that minimizes the total base station(BS)transmit power with constraints on signal-to-interference-plus-noise ratio(SINR) at the user terminals is considered. We show that this problem can be reformulated as a second order cone program(SOCP) with the same order of computational complexity as that of the non-robust precoder design. Next, a robust design of linear transceiver for MU-MIMO downlink is developed. This design is based on the minimization of sum mean square error(SMSE) with a constraint on the total BS transmit power, and assumes that the error in the CSI at the transmitter(CSIT) follows the stochastic error model. For this design, an iterative algorithm based on the associated Karush-Kuhn-Tucker(KKT) conditions is proposed. Our numerical results demonstrate the robust performance of the propose designs. Non-linear precoding: In this part, we consider robust designs of Tomlinson-Harashima precoders(THP) for MU-MISO and MU-MIMO downlinks with different performance criteria and CSI error models. For MU-MISO systems with imperfect CSIT, we investigate the problem of designing robust THPs under MSE and total BS transmit power constraints. The first design is based on the minimization of total BS transmit power under constraints on the MSE at the individual user receivers. We present an iterative procedure to solve this problem, where each iteration involves the solution of a pair of convex optimization problems. The second design is based on the minimization of a stochastic function of the SMSE under a constraint on the total BS transmit power. We solve this problem efficiently by the method of alternating optimization. For MU-MIMO downlink, we propose robust THP transceiver designs that jointly optimize the TH precoder and receiver filters. We consider these transceiver designs under stochastic and norm-bounded error models for CSIT. For the SE model, we propose a minimum SMSE transceiver design. For the NBE model, we propose three robust designs, namely, minimum SMSE design, MSE-constrained design, and MSE-balancing design. Our proposed solutions to these robust design problems are based on iteratively solving a pair of sub-problems, one of which can be solved analytically, and the other can be formulated as a convex optimization problem that can be solved efficiently. Robust precoder designs for non-regenerative relay networks: In this part, we consider robust designs for two scenarios in the case of relay-assisted networks. First, we consider a non-regenerative relay network with a source-destination node pair assisted by multiple relay nodes, where each node is equipped with a single antenna. The set of the cooperating relay nodes can be considered as a distributed antenna array. For this scenario, we present a robust distributed beam former design that minimizes the total relay transmit power with a constraint on the SNR at the destination node. We show that this robust design problem can be reformulated as a semi-definite program (SDP)that can be solved efficiently. Next, we consider a non-regenerative relay network, where a set of source-destination node pairs are assisted by a MIMO-relay node, which is equipped with multiple transmit and multiple receive antennas. For this case, we consider robust designs in the presence of stochastic and norm-bounded CSI errors. We show that these problems can be reformulated as convex optimization problems. In the case of norm-bounded error, we use an approximate expression for the MSE in order to obtain a tractable solution.

In this thesis, our focus is on low-complexity, high-performance detection algorithms for multi-antenna communication receivers. A key contribution in this thesis is the demonstration that efficient algorithms from metaheuristics and machine learning can be gainfully adapted for signal detection in multi- antenna communication receivers. We first investigate a popular metaheuristic known as the reactive tabu search (RTS), a combinatorial optimization technique, to decode the transmitted signals in large-dimensional communication systems. A basic version of the RTS algorithm is shown to achieve near-optimal performance for 4-QAM in large dimensions. We then propose a method to obtain a lower bound on the BER performance of the optimal detector. This lower bound is tight at moderate to high SNRs and is useful in situations where the performance of optimal detector is needed for comparison, but cannot be obtained due to very high computational complexity. To improve the performance of the basic RTS algorithm for higher-order modulations, we propose variants of the basic RTS algorithm using layering and multiple explorations. These variants are shown to achieve near-optimal performance in higher-order QAM as well. Next, we propose a new receiver called linear regression of minimum mean square error (MMSE) residual receiver (referred to as LRR receiver). The proposed LRR receiver improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel) to find the linear regression parameters. The LRR receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs well. Finally, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in ma- chine learning. We call our receiver boosted MMSE (B-MMSE) receiver. We demonstrate that the performance and complexity of the proposed B-MMSE receiver are quite attractive for multi-antenna communication receivers.

Approved for public release, distribution is unlimited / This thesis investigates the simulation performance of multiple-input multiple-output (MIMO) systems utilizing Alamoutibased space-time block coding (STBC) technique. The MIMO communication systems using STBC technique employing both single- carrier modulation and orthogonal frequency division multiplexing (OFDM) are simulated in Matlab. The physical layer part of the IEEE 802.16a standard is used in constructing the simulated OFDM schemes. Stanford University Interim (SUI) channel models are selected for the wireless channel in the simulation process. The performance results of the simulated MIMO systems are compared to those of conventional single antenna systems. / Lieutenant Junior Grade, Turkish Navy

Cette thèse a permis de travailler sur l'efficacité d'un canal des systèmes massifs MIMO pour lesquels il faille déterminer le débit à l'Uplink des terminaux présents dans leurs cellules respectives. Comme hypothèse, la bande de fréquence en mode TDD est réutilisée dans chaque cellule. Tous les symboles sont propagés de manière asynchrone par les terminaux présents dans les cellules, n'empêchant pas de fait des interactions intra et inter symboles au niveau des stations de base. Ces signaux rencontrent beaucoup d'obstacles sur leur trajet qui entraînent des retards, des pertes de signaux (destructifs), des régénérations de signaux (constructifs) avec divers types de modulation (amplitude, fréquentielle, phase), etc. L’affaiblissement du trajet dans le canal est mis en exergue avec les différentes valeurs prises par le coefficient d'atténuation choisi lors des simulations. Face à cette situation, il a fallu rechercher le meilleur et robuste estimateur de canal à un temps de cohérence donné. La méthode MMSE (Minimum Mean Square Error) est retenue, comparée à d'autres. Pour la performance des systèmes massifs MIMO, nous nous sommes appesantis sur les méthodes de diversité des antennes (diversité d'ordre N), les méthodes de coding, les méthodes d'accès OFDMA et les méthodes d'égalisation pour montrer qu'effectivement le fait d'utiliser de nombreuses antennes au niveau des stations de base améliore et contribue aux gains recherchés en débits. Avec les systèmes massifs MIMO, nous avons montré que l'apport antennaire est bien reconnu dans la gestion des interférences. Un algorithme de calcul de débit à l'Uplink a été réalisé avec trois récepteurs conventionnels que sont le MRC (Maximum Ratio Combiner), le ZF (Zero-Forcing) et le MMSE (Minimum Mean Square Error). Les simulations ont permis de comparer les différentes approches. En faisant varier la puissance de contamination des symboles pilotes, nous observons la convergence des courbes ZF et MMSE. Si le nombre des cellules L augmentent, nous constatons que plus la puissance de contamination des symboles pilotes (pp) est élevée, plus la capacité diminue dans le canal. Après plusieurs itérations, notre algorithme converge vers une asymptote (régime stationnaire et linéaire) où les échantillons à la sortie des détecteurs s’approchent de la séquence de données émises. Le SINR obtenu avec les détecteurs conventionnels permet le calcul des débits respectifs dans le canal avec le théorème de SHANNON. / This thesis made it possible to work on the efficiency of a channel of massive MIMO systems for which it is necessary to determine the throughput at the Uplink of the terminals present in their respective cells. As an assumption, the frequency band in TDD mode is reused in each cell. All symbols are propagated asynchronously by the terminals present in the cells, not effectively preventing intra- and inter-symbol interactions at the base stations. These signals encounter many obstacles on their path that lead to delays, signal losses (destructive), signal regenerations (constructive) with various types of modulation (amplitude, frequency, phase), etc. The path loss in the channel is highlighted with the different values taken by the attenuation coefficient chosen during the simulations. Faced with this situation, it was necessary to look for the best and most robust channel estimator at a given consistency time. The MMSE (Minimum Mean Square Error) method is used, compared to others. For the performance of massive MIMO systems, we have focused on antenna diversity methods (N-order diversity), coding methods, OFDMA access methods and equalization methods to show that effectively using multiple antennas at base stations improves and contributes to the desired rate gains. With massive MIMO systems, we have shown that antennar contribution is well recognized in interference management. An algorithm for calculating the flow rate at the Uplink was developed using three conventional receivers: the MRC (Maximum Ratio Combiner), the ZF (Zero-Forcing) and the MMSE (Minimum Mean Square Error). The simulations made it possible to compare the different approaches. By varying the contamination power of the pilot symbols, we observe the convergence of the ZF and MMSE curves. If the number of L cells increases, we find that the higher the contamination power of the pilot symbols (pp), the lower the capacity in the channel. After several iterations, our algorithm converges to an asymptote (stationary and linear regime) where the samples at the detector output approach the transmitted data sequence. The SINR obtained with conventional detectors allows the calculation of the respective flows in the channel with the SHANNON theorem.

Dans cette thèse, on étudie l'application de la méthode des moments pour les télécommunications. On analyse cette méthode et on montre son importance pour l'étude des matrices aléatoires. On utilise le cadre de probabilités libres pour analyser cette méthode. La notion de produit de convolution/déconvolution libre peut être utilisée pour prédire le spectre asymptotique de matrices aléatoires qui sont asymptotiquement libres. On montre que la méthode de moments est un outil puissant même pour calculer les moments/moments asymptotiques de matrices qui n'ont pas la propriété de liberté asymptotique. En particulier, on considère des matrices aléatoires gaussiennes de taille finie et des matrices de Vandermonde al ?eatoires. On développe en série entiére la distribution des valeurs propres de differents modèles, par exemple les distributions de Wishart non-centrale et aussi les distributions de Wishart avec des entrées corrélées de moyenne nulle. Le cadre d'inference pour les matrices des dimensions finies est suffisamment souple pour permettre des combinaisons de matrices aléatoires. Les résultats que nous présentons sont implémentés en code Matlab en générant des sous-ensembles, des permutations et des relations d'équivalence. On applique ce cadre à l'étude des réseaux cognitifs et des réseaux à forte mobilité. On analyse les moments de matrices de Vandermonde aléatoires avec des entrées sur le cercle unitaire. On utilise ces moments et les détecteurs à expansion polynomiale pour décrire des détecteurs à faible complexité du signal transmis par des utilisateurs mobiles à une station de base (ou avec deux stations de base) représentée par des réseaux linéaires uniformes. / In this thesis, we focus on the analysis of the moments method, showing its importance in the application of random matrices to wireless communication. This study is conducted in the free probability framework. The concept of free convolution/deconvolution can be used to predict the spectrum of sums or products of random matrices which are asymptotically free. In this framework, we show that the moments method is very appealing and powerful in order to derive the moments/asymptotic moments for cases when the property of asymptotic freeness does not hold. In particular, we focus on Gaussian random matrices with finite dimensions and structured matrices as Vandermonde matrices. We derive the explicit series expansion of the eigenvalue distribution of various models, as noncentral Wishart distributions, as well as correlated zero mean Wishart distributions. We describe an inference framework so flexible that it is possible to apply it for repeated combinations of random ma- trices. The results that we present are implemented generating subsets, permutations, and equivalence relations. We developped a Matlab routine code in order to perform convolution or deconvolution numerically in terms of a set of input moments. We apply this inference framework to the study of cognitive networks, as well as to the study of wireless networks with high mobility. We analyze the asymptotic moments of random Vandermonde matrices with entries on the unit circle. We use them and polynomial expansion detectors in order to design a low complexity linear MMSE decoder to recover the signal transmitted by mobile users to a base station or two base stations, represented by uniform linear arrays.

Large-dimensional communication systems are likely to play an important role in modern wireless communications, where dimensions can be in space, time, frequency and their combinations. Large dimensions can bring several advantages with respect to the performance of communication systems. Harnessing such large-dimension benefits in practice, however, is challenging. In particular, optimum signal detection gets prohibitively complex for large dimensions. Consequently, low-complexity detection techniques that scale well for large dimensions while achieving near-optimal performance are of interest. Belief Propagation (BP) is a technique that solves inference problems using graphical models. BP has been successfully employed in a variety of applications including computational biology, statistical signal/image processing, machine learning and artificial intelligence. BP is well suited in several communication problems as well; e.g., decoding of turbo codes and low-density parity check codes (LDPC), and multiuser detection. We propose a BP based algorithm for detection in large-dimension linear vector channels employing binary phase shift keying (BPSK) modulation, by adopting a Markov random field (MRF)graphical model of the system. The proposed approach is shown to achieve i)detection at low complexities that scale well for large dimensions, and ii)improved bit error performance for increased number of dimensions (a behavior we refer to as the ’large-system behavior’). As one application of the BP based approach, we demonstrate the effectiveness of the proposed BP algorithm for decoding non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA)having large dimensions. We further improve the performance of the proposed algorithm through damped belief propagation, where messages that are passed from one iteration to the next are formed as a weighted combination of messages from the current iteration and the previous iteration. Next, we extend the proposed BP approach to higher order modulation. through a novel scheme of interference cancellation. This proposed scheme exhibits large system behavior in terms of bit error performance, while being scalable to large dimensions in terms of complexity. Finally, as another application of the BP based approach, we illustrate the adoption and performance of the proposed BP algorithm for low-complexity near-optimal equalization in severely delay-spread UWBMIMO-ISI channels that are characterized by large number (tens to hundreds)of multipath components.

This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream. We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink. Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance. The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.

This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream. We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink. Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance. The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.