Multi-antenna wireless communication systems that employ a large number of antennas have recently stirred a lot of research interest. This is mainly due to the possibility of achieving very high spectral efficiency, power efficiency, and link reliability in such large-scale multiple-input multiple-output (MIMO) systems. An emerging architecture for large-scale multiuser MIMO communications is one where each base station (BS) is equipped with a large number of antennas (tens to hundreds of antennas) and the user terminals are equipped with fewer antennas (one to four antennas) each. The backhaul communication between base stations is also carried out using large number of antennas. Because of the high dimensionality of large-scale MIMO signals, the computational complexity of various transceiver operations can be prohibitively large. Therefore, low complexity techniques that scale well for transceiver signal processing in such large-scale MIMO systems are crucial. The transceiver operations of interest include signal encoding at the transmitter, and channel estimation, detection and decoding at the receiver. This thesis focuses on the design and analysis of novel low-complexity transceiver signal processing schemes for large-scale MIMO systems. In this thesis, we consider two types of large-scale MIMO systems, namely, massive MIMO systems and generalized spatial modulation MIMO (GSM-MIMO) systems. In massive MIMO, the mapping of information bits to modulation symbols is done using conventional modulation alphabets (e.g., QAM, PSK). In GSM-MIMO, few of the avail-able transmit antennas are activated in a given channel use, and information bits are conveyed through the indices of these active antennas, in addition to the bits conveyed through conventional modulation symbols. We also propose a novel modulation scheme named as precoder index modulation, where information bits are conveyed through the index of the chosen precoder matrix as well as the modulation symbols transmitted. Massive MIMO: In this part of the thesis, we propose a novel MIMO receiver which exploits channel hardening that occurs in large-scale MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of HH H become much weaker compared to the diagonal terms as the size of the channel gain matrix H increases. We exploit this phenomenon to devise a low-complexity channel estimation scheme and a message passing algorithm for signal detection at the BS receiver in massive MIMO systems. We refer to the proposed receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The key novelties in the proposed CHEMP receiver are: (i) operation on the matched filtered system model, (ii) Gaussian approximation on the off-diagonal terms of the HH H matrix, and (iii) direct estimation of HH H instead of H and use of this estimate of HH H for detection The performance and complexity results show that the proposed CHEMP receiver achieves near-optimal performance in large-scale MIMO systems at complexities less than those of linear receivers like minimum mean squared error (MMSE) receiver. We also present a log-likelihood ratio (LLR) analysis that provides an analytical reasoning for this better performance of the CHEMP receiver. Further, the proposed message passing based detection algorithm enables us to combine it with low density parity check (LDPC) decoder to formulate a joint message passing based detector-decoder. For this joint detector-decoder, we design optimized irregular binary LDPC codes specific to the massive MIMO channel and the proposed receiver through EXIT chart matching. The LDPC codes thus obtained are shown to achieve improved coded bit error rate (BER) performance compared to off-the-shelf irregular LDPC codes. The performance of the CHEMP receiver degrades when the system loading factor (ratio of the number of users to the number of BS antennas) and the modulation alpha-bet size are large. It is of interest to devise receiver algorithms that work well for high system loading factors and modulation alphabet sizes. For this purpose, we propose a low-complexity factor-graph based vector message passing algorithm for signal detection. This algorithm uses a scalar Gaussian approximation of interference on the basic sys-tem model. The performance results show that this algorithm performs well for large modulation alphabets and high loading factors. We combine this detection algorithm with a non-binary LDPC decoder to obtain a joint detector-decoder, where the field size of the non-binary LDPC code is same as the size of the modulation alphabet. For this joint message passing based detector-decoder, we design optimized non-binary irregular LDPC codes tailored to the massive MIMO channel and the proposed detector. GSM-MIMO: In this part of the thesis, we consider GSM-MIMO systems in point-to-point as well as multiuser communication settings. GSM-MIMO has the advantage of requiring only fewer transmit radio frequency (RF) chains than the number of transmit antennas. We analyze the capacity of point-to-point GSM-MIMO, and obtain lower and upper bounds on the GSM-MIMO system capacity. We also derive an upper bound on the BER performance of maximum likelihood detection in GSM-MIMO systems. This bound is shown to be tight at moderate to high signal-to-noise ratios. When the number of transmit and receive antennas are large, the complexity of en-coding and decoding of GSM-MIMO signals can be prohibitively high. To alleviate this problem, we propose a low complexity GSM-MIMO encoding technique that utilizes com-binatorial number system for bits-to-symbol mapping. We also propose a novel layered message passing (LaMP) algorithm for decoding GSM-MIMO signals. Low computational complexity is achieved in the LaMP algorithm by detecting the modulation bits and the antenna index bits in two deferent layers. We then consider large-scale multiuser GSM-MIMO systems, where multiple users employ GSM at their transmitters to communicate with a BS having a large number of receive antennas. For this system, we develop computationally efficient message passing algorithms for signal detection using vector Gaussian approximation of interference. The performance results of these algorithms show that the GSM-MIMO system outperforms the massive MIMO system by several dBs for the same spectral efficiency. Precoder index modulation: It is known that the performance of a communication link can be enhanced by exploiting time diversity without reducing the rate of transmission using pseudo random phase preceding (PRPP). In order to further improve the performance of GSM-MIMO, we apply PRPP technique to GSM-MIMO systems. PRPP provides additional diversity advantage at the receiver and further improves the performance of GSM-MIMO systems. For PRPP-GSM systems, we propose methods to simultaneously precode both the antenna index bits and the modulation symbols using rectangular precoder matrices. Finally, we extend the idea of index modulation to pre-coding and propose a new modulation scheme referred to as precoder index modulation (PIM). In PIM, information bits are conveyed through the index of a prehared PRPP matrix, in addition to the information bits conveyed through the modulation symbols. PIM is shown to increase the achieved spectral efficiency, in addition to providing diver-sity advantages.

Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexity SBL-based algorithms that exploit structured sparsity in the presence of fully/partially known measurement matrices. We apply the proposed algorithms to the problem of channel estimation and data detection in Orthogonal Frequency Division Multiplexing(OFDM) systems. Further, we derive Cram´er Rao type lower Bounds(CRB) for the single and multiple measurement vector SBL problem of estimating compressible vectors and their prior distribution parameters. The main contributions of the thesis are as follows: We derive Hybrid, Bayesian and Marginalized Cram´er Rao lower bounds for the problem of estimating compressible vectors drawn from a Student-t prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error(MSE) in the estimates. Through simulations, we demonstrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector. OFDM is a well-known multi-carrier modulation technique that provides high spectral efficiency and resilience to multi-path distortion of the wireless channel It is well-known that the impulse response of a wideband wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this thesis, we consider the estimation of the unknown channel coefficients and its support in SISO-OFDM systems using a SBL framework. We propose novel pilot-only and joint channel estimation and data detection algorithms in block-fading and time-varying scenarios. In the latter case, we use a first order auto-regressive model for the time-variations, and propose recursive, low-complexity Kalman filtering based algorithms for channel estimation. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the MSE and coded bit error rate performance. • Multiple Input Multiple Output(MIMO) combined with OFDM harnesses the inherent advantages of OFDM along with the diversity and multiplexing advantages of a MIMO system. The impulse response of wireless channels between the Nt transmit and Nr receive antennas of a MIMO-OFDM system are group approximately sparse(ga-sparse),i.e. ,the Nt Nr channels have a small number of significant paths relative to the channel delay spread, and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wire¬less channels are also group approximately-cluster sparse(ga-csparse),i.e.,every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this thesis, we cast the problem of estimating the ga-sparse and ga-csparse block-fading and time-varying channels using a multiple measurement SBL framework. We propose a bouquet of novel algorithms for MIMO-OFDM systems that generalize the algorithms proposed in the context of SISO-OFDM systems. The efficacy of the proposed techniques are demonstrated in terms of MSE and coded bit error rate performance.

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.

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.

Abstract Multiple-input multiple-output transmitted signal design for the partially coherent Rayleigh fading channels with discrete inputs under a given average transmit power constraint is consider in this thesis. The objective is to design the space-time constellations and linear precoders to adapt to the degradation caused by the imperfect channel estimation at the receiver and the transmit-receive antenna correlation. The system is partially coherent so that the multiple-input multiple-output channel coefficients are estimated at the receiver and its error covariance matrix is fed back to the transmitter. Two constellation design criteria, one for the single and another for the multiple transmit antennae are proposed. An upper bound on the average bit error probability for the single transmit antenna and cutoff rate, i.e., a lower bound on the mutual information, for multiple transmit antennae are derived. Both criteria are functions of channel estimation error covariance matrix. The designed constellations are called as partially coherent constellation. Additionally, to use the resulting constellations together with forward error control codes requires efficient bit mapping schemes. Because these constellations lack geometrical symmetry in general, the Gray mapping is not always possible in the majority of the constellations obtained. Moreover, different mapping schemes may lead to highly different bit error rate performances. Thus, an efficient bit mapping algorithm called the modified binary switching algorithm is proposed. It minimizes an upper bound on the average bit error probability. It is shown through computer simulations that the designed partially coherent constellation and their optimized bit mapping algorithm together with turbo codes outperform the conventional constellations. Linear precoder design was also considered as a simpler, suboptimal alternative. The cutoff rate expression is again used as a criterion to design the linear precoder. A linear precoder is obtained by numerically maximizing the cutoff rate with respect to the precoder matrix with a given average transmit power constraint. Furthermore, the precoder matrix is decomposed using singular-value-decomposition into the input shaping, power loading, and beamforming matrices. The beamforming matrix is found to coincide with the eigenvectors of the transmit correlation matrix. The power loading and input shaping matrices are solved numerically using the difference of convex functions programming algorithm and optimization under the unitary constraint, respectively. Computer simulations show that the performance gains of the designed precoders are significant compared to the cutoff rate optimized partially coherent constellations without precoding. / Tiivistelmä Väitöskirjassa tarkastellaan lähetyssignaalien suunnittelua osittain koherenteissa Rayleigh-häipyvissä kanavissa toimiviin monitulo-monilähtöjärjestelmiin (MIMO). Lähettimen keskimääräinen lähetysteho oletetaan rajoitetuksi ja lähetyssignaali diskreetiksi. Tavoitteena on suunnitella tila-aikakonstellaatioita ja lineaarisia esikoodereita jotka mukautuvat epätäydellisen kanavaestimoinnin aiheuttamaan suorituskyvyn heikkenemiseen sekä lähetin- ja vastaanotinantennien väliseen korrelaatioon. Tarkasteltavien järjestelmien osittainen koherenttisuus tarkoittaa sitä, että MIMO-kanavan kanavakertoimet estimoidaan vastaanottimessa, josta niiden virhekovarianssimatriisi lähetetään lähettimelle. Työssä esitetään kaksi konstellaatiosuunnittelukriteeriä, toinen yhdelle lähetinantennille ja toinen moniantennilähettimelle. Molemmat kriteerit ovat kanavan estimaatiovirheen kovarianssimatriisin funktioita. Työssä johdetaan yläraja keskimääräiselle bittivirhetodennäköisyydelle yhden lähetinantennin tapauksessa sekä rajanopeus (cutoff rate), joka on alaraja keskinäisinformaatiolle, usean lähetinantennin tapauksessa. Konstellaatioiden käyttö yhdessä virheenkorjauskoodien kanssa edellyttää tehokaita menetelmiä, joilla bitit kuvataan konstellaatiopisteisiin. Koska tarvittavat konstellaatiot eivät ole tyypillisesti geometrisesti symmetrisiä, Gray-kuvaus ei ole yleensä mahdollinen.Lisäksi erilaiset kuvausmenetelmät voivat johtaa täysin erilaisiin bittivirhesuhteisiin. Tästä johtuen työssä esitetään uusi kuvausalgoritmi (modified bit switching algorithm), joka minimoi keskimääräisen bittivirhetodennäköisyyden ylärajan. Simulointitulokset osoittavat, että työssä kehitetyt konstellaatiot antavat paremman suorituskyvyn turbokoodatuissa järjestelmissä kuin perinteiset konstellaatiot. Työssä tarkastellaan myös lineaarista esikoodausta yksinkertaisena, alioptimaalisena vaihtoehtona uusille konstellaatioille. Esikoodauksen suunnittelussa käytetään samaa kriteeriä kuin konstellaatioiden kehityksessä eli rajanopeutta. Lineaarinen esikooderi löydetään numeerisesti maksimoimalla rajanopeus kun rajoitusehtona on lähetysteho. Esikoodausmatriisi hajotetaan singulaariarvohajotelmaa käyttäen esisuodatus, tehoallokaatio ja keilanmuodostusmatriiseiksi, jonka havaitaan vastaavan lähetyskorrelaatiomatriisin ominaisvektoreita. Tehoallokaatiomatriisi ratkaistaan numeerisesti käyttäen difference of convex functions -optimointia ja esisuodatusmatriisi optimoinnilla unitaarista rajoitusehtoa käyttäen. Simulaatiotulokset osoittavat uusien esikoodereiden tarjoavan merkittävän suorituskykyedun sellaisiin rajanopeusoptimoituihin osittain koherentteihin konstellaatioihin nähden, jotka eivät käytä esikoodausta.

One of the most popular ways to exploit the advantages of a multiple-input multiple-output (MIMO) system is using space time block coding. A space time block code (STBC) is a finite set of complex matrices whose entries consist of the information symbols to be transmitted. A linear STBC is one in which the information symbols are linearly combined to form a two-dimensional code matrix. A well known method of maximum-likelihood (ML) decoding of such STBCs is using the sphere decoder (SD). In this thesis, new constructions of STBCs with low sphere decoding complexity are presented and various ways of characterizing and reducing the sphere decoding complexity of an STBC are addressed. The construction of low sphere decoding complexity STBCs is tackled using irreducible matrix representations of Clifford algebras, cyclic division algebras and crossed-product algebras. The complexity reduction algorithms for the STBCs constructed are explored using tree based search algorithms. Considering an STBC as a vector space over the set of weight matrices, the problem of characterizing the sphere decoding complexity is addressed using quadratic form representations. The main results are as follows. A sub-class of fast decodable STBCs known as Block Orthogonal STBCs (BOSTBCs) are explored. A set of sufficient conditions to obtain BOSTBCs are explained. How the block orthogonal structure of these codes can be exploited to reduce the SD complexity of the STBC is then explained using a depth first tree search algorithm. Bounds on the SD complexity reduction and its relationship with the block orthogonal structure are then addressed. A set of constructions to obtain BOSTBCs are presented next using Clifford unitary weight designs (CUWDs), Coordinate-interleaved orthogonal designs (CIODs), cyclic division algebras and crossed product algebras which show that a lot of codes existing in literature exhibit the block orthogonal property. Next, the dependency of the ordering of information symbols on the SD complexity is discussed following which a quadratic form representation known as the Hurwitz-Radon quadratic form (HRQF) of an STBC is presented which is solely dependent on the weight matrices of the STBC and their ordering. It is then shown that the SD complexity is only a function of the weight matrices defining the code and their ordering, and not of the channel realization (even though the equivalent channel when SD is used depends on the channel realization). It is also shown that the SD complexity is completely captured into a single matrix obtained from the HRQF. Also, for a given set of weight matrices, an algorithm to obtain a best ordering of them leading to the least SD complexity is presented using the HRQF matrix.

[pt] Na primeira parte desta tese, são desenvolvidos dois esquemas de detecção por listas para sistemas MIMO multiusuário. As técnicas propostas usam uma única transformação de redução de reticulado (LR) para modificar a matriz de canal entre os usuários e a estação base (BS). Após a transformação LR, um candidato confiável do sinal transmitido é obtido usando um detector de cancelamento sucessivo de interferências (SIC). No detector em múltiplos ramos com redução de reticulado e cancelamento sucessivo de interferências (MB-LR-SIC) proposto, um número fixo de diferentes ordenamentos para o detector SIC gera uma lista de possíveis candidatos para a informação transmitida. O melhor candidato é escolhido usando o critério maximum likelihood (ML). No detector por listas de tamanho variável (VLD) proposto, um algoritmo que decide se o candidato atual tem uma boa qualidade ou se é necessário continuar procurando por um candidato melhor nos ordenamentos restantes é utilizado. Os resultados numéricos mostram que os esquemas propostos têm um desempenho quase ótimo com uma complexidade computacional bem abaixo do detector ML. Um esquema de detecção e decodificação iterativa (IDD) baseado no algoritmo VLD é também desenvolvido, produzindo um desempenho próximo a um sistema mono usuário (SU) livre de interferências. Na segunda parte desta tese, uma técnica de detecção desacoplada de sinais (DSD) para sistemas MIMO massivo é proposta. Esta técnica permite que o sinal composto recebido na BS seja separado em sinais independentes, correspondentes a diferentes classes de usuários, viabilizando assim o uso dos procedimentos de detecção propostos na primeira parte desta tese em sistemas MIMO massivos. Um modelo de sinais para sistemas MIMO massivo com antenas centralizadas e/ou antenas distribuídas operando em redes heterogêneas de quinta geração é proposto. Uma análise baseada na soma das taxas e um estudo de custo computacional para DSD são apresentados. Os resultados numéricos ilustram o excelente compromisso desempenho versus complexidade obtido com a técnica DSD quando comparada com o esquema de detecção conjunta tradicional. / [en] In the first part of this thesis, we introduce two list detection schemes for the uplink scenario of multiuser multiple-input multiple-output (MUMIMO) systems. The proposed techniques employ a single lattice reduction (LR) transformation to modify the channel matrix between the users and the base station (BS). After the LR transformation, a reliable candidate for the transmitted signal vector, provided by successive interference cancellation (SIC) detection is obtained. In the proposed multi-branch lattice reduction SIC (MB-LR-SIC) detector, a fixed number of different orderings, generates a list of SIC detection candidates. The best candidate is chosen according to the maximum likelihood (ML) selection criterion. For the proposed variable list detection (VLD) scheme, an algorithm to decide if the current candidate has good quality or if it is necessary to further explore different orderings to improve the detection performance is employed. Simulation results indicate that the proposed schemes have a near-optimal performance while keeping its computational complexity well below that of the ML detector. An iterative detection and decoding (IDD) scheme based on the VLD algorithm is also developed, producing an excellent performance that approaches the single user (SU) scenario. In the second part of this thesis, a decoupled signal detection (DSD) technique which allows the separation of uplink signals, for each user class, at the base station (BS) for massive MIMO systems is proposed. The proposed DSD allows to implement the detection procedures proposed in the first part of this thesis in massive MIMO scenarios. A mathematical signal model for massive MIMO systems with centralized and distributed antennas in the future fifth generation (5G) heterogeneous cellular networks is also developed. A sum-rate analysis and a study of computational cost for DSD are also presented. Simulation results show excellent performance of the proposed DSD algorithm when combined with linear and SIC-based detectors.

[EN] Multiple-Input Multiple-Output (MIMO) technology in Digital Terrestrial Television (DTT) networks has the potential to increase the spectral efficiency and improve network coverage to cope with the competition of limited spectrum use (e.g., assignment of digital dividend and spectrum demands of mobile broadband), the appearance of new high data rate services (e.g., ultra-high definition TV - UHDTV), and the ubiquity of the content (e.g., fixed, portable, and mobile). It is widely recognised that MIMO can provide multiple benefits such as additional receive power due to array gain, higher resilience against signal outages due to spatial diversity, and higher data rates due to the spatial multiplexing gain of the MIMO channel. These benefits can be achieved without additional transmit power nor additional bandwidth, but normally come at the expense of a higher system complexity at the transmitter and receiver ends. The final system performance gains due to the use of MIMO directly depend on physical characteristics of the propagation environment such as spatial correlation, antenna orientation, and/or power imbalances experienced at the transmit aerials. Additionally, due to complexity constraints and finite-precision arithmetic at the receivers, it is crucial for the overall system performance to carefully design specific signal processing algorithms. This dissertation focuses on transmit and received signal processing for DTT systems using MIMO-BICM (Bit-Interleaved Coded Modulation) without feedback channel to the transmitter from the receiver terminals. At the transmitter side, this thesis presents investigations on MIMO precoding in DTT systems to overcome system degradations due to different channel conditions. At the receiver side, the focus is given on design and evaluation of practical MIMO-BICM receivers based on quantized information and its impact in both the in-chip memory size and system performance. These investigations are carried within the standardization process of DVB-NGH (Digital Video Broadcasting - Next Generation Handheld) the handheld evolution of DVB-T2 (Terrestrial - Second Generation), and ATSC 3.0 (Advanced Television Systems Committee - Third Generation), which incorporate MIMO-BICM as key technology to overcome the Shannon limit of single antenna communications. Nonetheless, this dissertation employs a generic approach in the design, analysis and evaluations, hence, the results and ideas can be applied to other wireless broadcast communication systems using MIMO-BICM. / [ES] La tecnología de múltiples entradas y múltiples salidas (MIMO) en redes de Televisión Digital Terrestre (TDT) tiene el potencial de incrementar la eficiencia espectral y mejorar la cobertura de red para afrontar las demandas de uso del escaso espectro electromagnético (e.g., designación del dividendo digital y la demanda de espectro por parte de las redes de comunicaciones móviles), la aparición de nuevos contenidos de alta tasa de datos (e.g., ultra-high definition TV - UHDTV) y la ubicuidad del contenido (e.g., fijo, portable y móvil). Es ampliamente reconocido que MIMO puede proporcionar múltiples beneficios como: potencia recibida adicional gracias a las ganancias de array, mayor robustez contra desvanecimientos de la señal gracias a la diversidad espacial y mayores tasas de transmisión gracias a la ganancia por multiplexado del canal MIMO. Estos beneficios se pueden conseguir sin incrementar la potencia transmitida ni el ancho de banda, pero normalmente se obtienen a expensas de una mayor complejidad del sistema tanto en el transmisor como en el receptor. Las ganancias de rendimiento finales debido al uso de MIMO dependen directamente de las características físicas del entorno de propagación como: la correlación entre los canales espaciales, la orientación de las antenas y/o los desbalances de potencia sufridos en las antenas transmisoras. Adicionalmente, debido a restricciones en la complejidad y aritmética de precisión finita en los receptores, es fundamental para el rendimiento global del sistema un diseño cuidadoso de algoritmos específicos de procesado de señal. Esta tesis doctoral se centra en el procesado de señal, tanto en el transmisor como en el receptor, para sistemas TDT que implementan MIMO-BICM (Bit-Interleaved Coded Modulation) sin canal de retorno hacia el transmisor desde los receptores. En el transmisor esta tesis presenta investigaciones en precoding MIMO en sistemas TDT para superar las degradaciones del sistema debidas a diferentes condiciones del canal. En el receptor se presta especial atención al diseño y evaluación de receptores prácticos MIMO-BICM basados en información cuantificada y a su impacto tanto en la memoria del chip como en el rendimiento del sistema. Estas investigaciones se llevan a cabo en el contexto de estandarización de DVB-NGH (Digital Video Broadcasting - Next Generation Handheld), la evolución portátil de DVB-T2 (Second Generation Terrestrial), y ATSC 3.0 (Advanced Television Systems Commitee - Third Generation) que incorporan MIMO-BICM como clave tecnológica para superar el límite de Shannon para comunicaciones con una única antena. No obstante, esta tesis doctoral emplea un método genérico tanto para el diseño, análisis y evaluación, por lo que los resultados e ideas pueden ser aplicados a otros sistemas de comunicación inalámbricos que empleen MIMO-BICM. / [CA] La tecnologia de múltiples entrades i múltiples eixides (MIMO) en xarxes de Televisió Digital Terrestre (TDT) té el potencial d'incrementar l'eficiència espectral i millorar la cobertura de xarxa per a afrontar les demandes d'ús de l'escàs espectre electromagnètic (e.g., designació del dividend digital i la demanda d'espectre per part de les xarxes de comunicacions mòbils), l'aparició de nous continguts d'alta taxa de dades (e.g., ultra-high deffinition TV - UHDTV) i la ubiqüitat del contingut (e.g., fix, portàtil i mòbil). És àmpliament reconegut que MIMO pot proporcionar múltiples beneficis com: potència rebuda addicional gràcies als guanys de array, major robustesa contra esvaïments del senyal gràcies a la diversitat espacial i majors taxes de transmissió gràcies al guany per multiplexat del canal MIMO. Aquests beneficis es poden aconseguir sense incrementar la potència transmesa ni l'ample de banda, però normalment s'obtenen a costa d'una major complexitat del sistema tant en el transmissor com en el receptor. Els guanys de rendiment finals a causa de l'ús de MIMO depenen directament de les característiques físiques de l'entorn de propagació com: la correlació entre els canals espacials, l'orientació de les antenes, i/o els desequilibris de potència patits en les antenes transmissores. Addicionalment, a causa de restriccions en la complexitat i aritmètica de precisió finita en els receptors, és fonamental per al rendiment global del sistema un disseny acurat d'algorismes específics de processament de senyal. Aquesta tesi doctoral se centra en el processament de senyal tant en el transmissor com en el receptor per a sistemes TDT que implementen MIMO-BICM (Bit-Interleaved Coded Modulation) sense canal de tornada cap al transmissor des dels receptors. En el transmissor aquesta tesi presenta recerques en precoding MIMO en sistemes TDT per a superar les degradacions del sistema degudes a diferents condicions del canal. En el receptor es presta especial atenció al disseny i avaluació de receptors pràctics MIMO-BICM basats en informació quantificada i al seu impacte tant en la memòria del xip com en el rendiment del sistema. Aquestes recerques es duen a terme en el context d'estandardització de DVB-NGH (Digital Video Broadcasting - Next Generation Handheld), l'evolució portàtil de DVB-T2 (Second Generation Terrestrial), i ATSC 3.0 (Advanced Television Systems Commitee - Third Generation) que incorporen MIMO-BICM com a clau tecnològica per a superar el límit de Shannon per a comunicacions amb una única antena. No obstant açò, aquesta tesi doctoral empra un mètode genèric tant per al disseny, anàlisi i avaluació, per la qual cosa els resultats i idees poden ser aplicats a altres sistemes de comunicació sense fils que empren MIMO-BICM. / Vargas Paredero, DE. (2016). Transmit and Receive Signal Processing for MIMO Terrestrial Broadcast Systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/66081 / TESIS / Premios Extraordinarios de tesis doctorales

Over recent years, the research and studies have focused on innovative solutions in various aspects and phases related to the high demands on data rate and energy for fifth-generation and beyond (B5G). This thesis aims to improve the energy efficiency, error rates, low-resolution ADCs/DACs, antenna array structures and sum-rate performances of a single cell downlink broadband millimetre-wave (mmWave) systems with orthogonal frequency division multiplexing (OFDM) modulation and deploying multi-user massive multiple inputs multiple outputs (MU mMIMO) by applying robust beamforming techniques and detection algorithms that support multiple streams per user (UE) in various environments and scenarios to achieve low complexity system design with reliable performance and significant improvement in users perceived quality of service (QoS). The performance of the four 5G candidate mmWave frequencies, 28 GHz, 39 GHz, 60 GHz, and 73 GHz, are investigated for indoor/outdoor propagation scenarios, including path loss models and multipath delay spread values. Results are compared to confirm that the received power and delay spread is decreased with increasing frequency. The results were also validated with the measurement findings for 60 GHz. Then several proposed design models of beamforming are studied and implemented modified algorithms of Hybrid Beamforming (HBF) approaches in indoor/outdoor scenarios over large scale fading wideband mmWave /Raleigh channels. Firstly, three beamforming based diagonalize the Equivalent Virtual Channel Matrix (EVCM) schemes with the optimal linear combining methods are presented to overcoming the self-interference problems in Quasi-Orthogonal-Space Time Block Code (QO-STBC) systems over narrowband mmWave Single-User mMIMO (SU mMIMO). The evaluated results show that the proposed beamforming based- Single Value Decomposition (SVD) outperforms the conventional beamforming and standard QO-STBC techniques in terms of BER and spectrum efficiency. Next, the proposed HBF algorithm approaches with the fully/ partially connected structures are developed and applied for sum-rate and symbol error rate (SER) performance maximization MU mMIMO-OFDM system, including HBF based on block diagonalization (BD) method Constraint/Unconstraint RF Power, Codebook, Kalman schemes. In addition, the modified near optimal linear HBF-Zero Forcing (HBF-ZF) and HBF-Minimum Mean Square Error (HBF MMSE) schemes, considering both fully-connected and partially-connected structures. Finally, Simulation results using MATLAB platform, demonstrate that the proposed HBF based codebook and most likely HBF based-unconstraint RF power algorithms achieve significant performance gains in terms SER and sum-rate efficiency as well as show high immunity against the deformities and disturbances in the system compared with other HBF algorithm schemes. / Ministry of Higher Education and Scientific Research, the Republic of Iraq

En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no lineales, como las series de Volterra, los polinómios y las redes neuronales. Los métodos kernel también presentan ciertos inconvenientes que deben ser abordados adecuadamente en las distintas aplicaciones, por ejemplo, las dificultades asociadas al manejo de grandes conjuntos de datos y los problemas de sobreajuste ocasionados al trabajar en espacios de dimensionalidad infinita.En este trabajo se desarrolla un conjunto de algoritmos basados en métodos kernel para resolver una serie de problemas no lineales, dentro del ámbito del procesado de señal y las comunicaciones. En particular, se tratan problemas de identificación e igualación de sistemas no lineales, y problemas de separación ciega de fuentes no lineal ("blind source separation", BSS). Esta tesis se divide en tres partes. La primera parte consiste en un estudio de la literatura sobre los métodos kernel. En la segunda parte, se proponen una serie de técnicas nuevas basadas en regresión con kernels para resolver problemas de identificación e igualación de sistemas de Wiener y de Hammerstein, en casos supervisados y ciegos. Como contribución adicional se estudia el campo del filtrado adaptativo mediante kernels y se proponen dos algoritmos recursivos de mínimos cuadrados mediante kernels ("kernel recursive least-squares", KRLS). En la tercera parte se tratan problemas de decodificación ciega en que las fuentes son dispersas, como es el caso en comunicaciones digitales. La dispersidad de las fuentes se refleja en que las muestras observadas se agrupan, lo cual ha permitido diseñar técnicas de decodificación basadas en agrupamiento espectral. Las técnicas propuestas se han aplicado al problema de la decodificación ciega de canales MIMO rápidamente variantes en el tiempo, y a la separación ciega de fuentes post no lineal. / In the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of reproducing kernel Hilbert spaces (RKHS), kernel methods yield convex optimization problems. In addition, they are universal nonlinear approximators and require only moderate computational complexity. These properties make them an attractive alternative to traditional nonlinear techniques such as Volterra series, polynomial filters and neural networks.This work aims to study the application of kernel methods to resolve nonlinear problems in signal processing and communications. Specifically, the problems treated in this thesis consist of the identification and equalization of nonlinear systems, both in supervised and blind scenarios, kernel adaptive filtering and nonlinear blind source separation.In a first contribution, a framework for identification and equalization of nonlinear Wiener and Hammerstein systems is designed, based on kernel canonical correlation analysis (KCCA). As a result of this study, various other related techniques are proposed, including two kernel recursive least squares (KRLS) algorithms with fixed memory size, and a KCCA-based blind equalization technique for Wiener systems that uses oversampling. The second part of this thesis treats two nonlinear blind decoding problems of sparse data, posed under conditions that do not permit the application of traditional clustering techniques. For these problems, which include the blind decoding of fast time-varying MIMO channels, a set of algorithms based on spectral clustering is designed. The effectiveness of the proposed techniques is demonstrated through various simulations.