The Orthogonal frequency division multiplexing (OFDM) technique has been intensively used in many wireless communication systems to achieve higher data rate transmissions. Due to the fact that the OFDM technique entails redundant block transmissions; the transmitted blocks suffer from the inter-symbol interference (ISI) and inter-block interference (IBI). To compensate this serious effect, in many literatures redundant symbols (or guard interval) with adequate length are inserted in the transmitted symbols to prevent the IBI. Also, in the receiver the equalizer can be employed to deal with ISI. In this thesis, we present a new pseudo random cyclic-postfix (PRCP-) OFDM associated with the multiple-input multiple-output (MIMO) antenna system configuration to further improve the system performance. In fact, the MIMO system can enhance channel capacity and achieve high data-rate. The above-mentioned PRCP-OFDM technique combines with the MIMO antennas system, through the appropriate model design can be used to combat the multi-path effect or the inter-block interference. As evident from the simulation results, the proposed ST-BC MIMO PRCP-OFDM system can avoid the interference of transmitted signals during the estimation of channel impulse response (CIR) with proposed cyclic-postfix sequences. In addition, to further improve and eliminate the residual IBI and ICI, the equalizer with the framework of the generalized sidelobe canceller (GSC) is considered. Specifically, when SNR grows, the proposed ST-BC MIMO PRCP-OFDM system can perform successfully in terms of symbol-error rate and semi-blind channel estimation. This is verified via the computer simulations.

Zugl.: Ilmenau, Techn. Univ., Diss., 2009

The demand of different multimedia services and different internet supported applications on mobile devices requires a high speed data rate and good service of quality. This can be obtained by implementing multiple Antenna technology on both stations i.e. User terminal and base station with an appropriate coding technique, and on the other hand MIMO can fulfill 3G & 4G demand and standard with a combination of other techniques. The MIMO diversity and MIMO multiplexing are the key factors to discuss and matter of concern is to achieve and support high speed data rate. MIMO multiplexing is a way to gain robustness and achievement in speed of data information. This thesis work describes a brief overview of WiMAX technology and MIMO-OFDM system and it also discusses the simplest Space time block code (STBC) known as Alamouti Space Time Code. The research approach is a literary survey to have theoretical understanding of the MIMO-OFDM system and WiMAX. The system‘s error performance is analyzed through simulation which showed the simulated results of Multi-Rate Resource Control (MRRC) scheme and Alamouti scheme are identical. And also the Bit Error Rate (BER) were checked for different MIMO systems, the simulation results shows that the BER improved to agreeable value also gains maximum diversity when the number of antennas increased on the receiver side. By improving the BER, we will get the better QoS. Matlab simulation has been performed, and presented the results, which shows the considerable error free transmission (FEC) for MIMO systems in WiMAX technology.

Machine learning (ML) has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training data in communication networks, especially in physical and MAC layer, are extremely limited; 2). The high-dynamic wireless environment and fast changing transmission schemes in communication networks make offline training impractical; 3). ML tools are treated as black boxes, which lack of explainability. This dissertation tries to address those challenges by selecting training-efficient neural networks, devising online training frameworks for wireless communication scenarios, and incorporating communication domain knowledge into the algorithm design. Training-efficient ML algorithms are customized for three communication applications: 1). Symbol detection, where real-time online learning-based symbol detection algorithms are designed for MIMO-OFDM and massive MIMO-OFDM systems by utilizing reservoir computing, extreme learning machine, multi-mode reservoir computing, and StructNet; 2) Channel estimation, where residual learning-based offline method is introduced for WiFi-OFDM systems, and a StructNet-based online method is devised for MIMO-OFDM systems; 3) Radio resource management, where reinforcement learning-based schemes are designed for dynamic spectrum access, as well as ORAN intelligent network slicing management. All algorithms introduced in this dissertation have demonstrated outstanding performance in their application scenarios, which paves the path for adopting ML-based solutions in practical wireless networks. / Doctor of Philosophy / Machine learning (ML), which is a branch of computer science that trains machine how to learn a solution from data, has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training issue: unlike areas such as computer vision where large amount of training data are available, the training data in communication systems are limited; 2). Uncertainty in generalization: ML usually requires offline training, where the ML models are trained by artificially generated offline data, with the assumption that offline training data have the same statistical property as the online testing one. However, when they are statistically different, the testing performance can not be guaranteed; 3). Lack of explainability, usually ML tools are treated as black boxes, whose behaviors can hardly be explained in an analytical way. When designed for wireless networks, it is desirable for ML to have similar levels of explainability as conventional methods. This dissertation tries to address those challenges by selecting training-efficient neural networks, devising online training frameworks for wireless communication scenarios, and incorporating communication domain knowledge into the algorithm design. Training-efficient ML algorithms are customized for three communication applications: 1). Symbol detection, which is a critical step of wireless communication receiver processing, it aims to recover the transmitted signals from the corruption of undesired wireless channel effects and hardware impairments; 2) Channel estimation, where transmitter transmits a special type of symbol called pilot whose value and position are known for the receiver, receiver estimates the underlying wireless channel by comparing the received symbols with the known pilots information; 3) Radio resource management, which allocates wireless resources such bandwidth and time slots to different users. All algorithms introduced in this dissertation have demonstrated outstanding performance in their application scenarios, which paves the path for adopting ML-based solutions in practical wireless networks.

Bridging machine learning technologies to multiple-input-multiple-output (MIMO) communications systems is a primary driving force for next-generation wireless systems. This dissertation introduces a variety of neural network structures for symbol detection/equalization tasks in MIMO systems configured with two different waveforms, orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency and space (OTFS). The former one is the major air interface in current cellular systems. The latter one is developed to handle high mobility. For the sake of real-time processing, the introduced neural network structures are incorporated with inductive biases of wireless communications signals and operate in an online training manner. The utilized inductive priors include the shifting invariant property of quadrature amplitude modulation, the time-frequency relation inherent in OFDM signals, the multi-mode feature of massive antennas, and the delay-Doppler representation of doubly selective channel. In addition, the neural network structures are rooted in reservoir computing - an efficient neural network computational framework with decent generalization performance for limited training datasets. Therefore, the resulting neural network structures can learn beyond observation and offer decent transmission reliability in the low signal-to-noise ratio (SNR) regime. This dissertation includes comprehensive simulation results to justify the effectiveness of the introduced NN architectures compared with conventional model-based approaches and alternative neural network structures. / Doctor of Philosophy / An important topic for next-generation wireless systems is the integration of machine learning technologies with conventional communications systems. This dissertation introduces several neural network architectures to solve the transmission problems in wireless communications systems. The discussion focuses on the following major modern communications technologies: multiple-input-multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM), and orthogonal time frequency space (OTFS). In today's cellular networks, MIMO and OFDM are the major air-interface. OTFS is a novel technique that has been designed to work in a high-mobility setting. The implemented neural network structures are integrated with inductive biases of wireless communications signals and operate in an online training mode with limited training datasets. The neural network architectures, in particular, are based on reservoir computing, which is an efficient neural network computational system. A learning algorithm's inductive bias (also known as learning bias) is a collection of assumptions that the learner makes to infer outputs from unknown inputs. The dissertation introduces four different inductive priors from four different perspectives of MIMO communications systems. As a result, the neural network architectures can learn beyond observation and provide good generalization output in scenarios having model mismatch issues. The dissertation provides extensive simulation results to support the efficacy of the implemented NN architectures compared to alternative neural network models and traditional model-based approaches.

Cette thèse est consacrée à l'amélioration des performances de codage/décodage de systèmes de transmission d'images fixes sur des canaux bruités et réalistes. Nous proposons, à cet effet, le développement de méthodes de transmission d'images optimisées en se focalisant sur les deux couches application et physique des réseaux sans fil. Au niveau de la couche application et afin d'assurer une bonne qualité de service, on utilise des algorithmes de compression efficaces permettant au récepteur de reconstruire l'image avec un maximum de fidélité (JPEG2000 et JPWL). Afin d'assurer une transmission sur des canaux sans fil avec un minimum de TEB à la réception, des techniques de transmission, de codage et de modulation avancées sont utilisées au niveau de la couche physique (système MIMO-OFDM, modulation adaptative, CCE, etc). Dans un premier temps, nous proposons un système de transmission robuste d'images codées JPWL intégrant un schéma de décodage conjoint source-canal basé sur des techniques de décodage à entrées pondérées. On considère, ensuite, l'optimisation d'une chaîne de transmission d'images sur un canal MIMO-OFDM sans fil réaliste. La stratégie de transmission d'images optimisée s'appuie sur des techniques de décodage à entrées pondérées et une approche d'adaptation de lien. Ainsi, le schéma de transmission proposé offre la possibilité de mettre en oeuvre conjointement de l'UEP, de l'UPA, de la modulation adaptative, du codage de source adaptatif et de décodage conjoint pour améliorer la qualité de l'image à la réception. Dans une seconde partie, nous proposons un système robuste de transmission de flux progressifs basé sur le principe de turbo décodage itératif de codes concaténés offrant une stratégie de protection inégale de données. Ainsi, l'originalité de cette étude consiste à proposer des solutions performantes d'optimisation globale d'une chaîne de communication numérique pour améliorer la qualité de transmission. / This work is devoted to improve the coding/ decoding performance of a transmission scheme over noisy and realistic channels. For this purpose, we propose the development of optimized image transmission methods by focusing on both application and physical layers of wireless networks. In order to ensure a better quality of services, efficient compression algorithms (JPEG2000 and JPWL) are used in terms of the application layer enabling the receiver to reconstruct the images with maximum fidelity. Furthermore, to insure a transmission on wireless channels with a minimum BER at reception, some transmission, coding and advanced modulation techniques are used in the physical layer (MIMO-OFDM system, adaptive modulation, FEC, etc). First, we propose a robust transmission system of JPWL encoded images integrating a joint source-channel decoding scheme based on soft input decoding techniques. Next, the optimization of an image transmission scheme on a realistic MIMO-OFDM channel is considered. The optimized image transmission strategy is based on soft input decoding techniques and a link adaptation approach. The proposed transmission scheme offers the possibility of jointly implementing, UEP, UPA, adaptive modulation, adaptive source coding and joint decoding strategies, in order to improve the image visual quality at the reception. Then, we propose a robust transmission system for embedded bit streams based on concatenated block coding mechanism offering an unequal error protection strategy. Thus, the novelty of this study consists in proposing efficient solutions for the global optimization of wireless communication system to improve transmission quality.

Ce travail de thèse s'intéresse à l'optimisation des performances de transmissions multimédias par une approche originale combinant des circuits radiofréquences, tel que l'am-plificateur de puissance et les distorsions du canal de transmission. Les signaux OFDM sont très sensibles aux non-linéarités de l'amplificateur à cause des fortes fluctuations du niveau du signal, caractérisées par le PAPR. Afin de réduire le PAPR, on propose tout d'abord d'améliorer la méthode TR en termes de rapidité de convergence et de réduction du PAPR, en comparant plusieurs algorithmes d'optimisation. On montre que l'algorithme du gradient con-jugué offre les meilleures performances tout en respectant les spécifications fréquentielles du standard IEEE 802.11a. Par la suite, la méthode TR est évaluée expérimentalement en pré-sence d'un amplificateur de puissance (SZP-2026Z) en utilisant un banc de mesures. On montre ainsi que la méthode TR permet une amélioration de la qualité de transmission. Cette amélioration peut être utilisée pour modifier le point de fonctionnement de l'amplificateur et per-mettre ainsi une réduction de 18 % de la puissance consommée. Les résultats expérimentaux ont conduit au choix d'un modèle réaliste d'amplificateur en considérant les effets mémoires. Ce dernier a été intégré dans une chaîne de simulation SISO comprenant également un modèle réaliste de canal de transmission. La chaîne décrite a permis d'évaluer les performances de la méthode TR dans des conditions de transmission réalistes. Enfin, on propose d'appliquer la méthode TR dans une chaîne MIMO-OFDM en boucle fermée dédiée à la transmission de contenus multimédias scalables dans un environnement réaliste, en utilisant le standard IEEE 802.11n. Cette étude présente une évaluation originale de l'impact de la méthode TR sur la qualité visuelle des images transmises, en prenant en compte le contenu multimédia, la non-linéarité de l'amplificateur et les distorsions apportées par le canal. / In this thesis, we are interested on the performances optimization of multimedia transmissions systems with an original contribution combining RF circuits' imperfections presented by the power amplifier (PA) nonlinearities and the transmission channel distortions. The studied system uses the OFDM technique which is the most widespread multicarrier modulation in recent radio communications systems. However, its major drawback is the high PAPR value, which degrades the transmission quality due to the PA nonlinearities. To reduce the PAPR, we first propose to improve the TR method in terms of convergence speed and PAPR reduction, by studying several optimization algorithms. We show that the conjugate gradient algorithm provides the best performance while respecting the frequency specifica-tions of the IEEE 802.11a standard. Thereafter, TR method has been evaluated experimentally in the presence of a commercial PA (SZP-2026Z) and using a measurement bench. It is shown that the TR method improves the quality of service (QoS), with 18% reduction in PA power consumption. The experimental study has resulted to choosing a realistic PA model consider-ing memory effects. This PA model has been integrated into a SISO simulation chain includ-ing also a realistic channel model. This chain is used to evaluate the TR method performances under realistic transmission conditions. Finally, we propose to apply the TR method in a closed-loop MIMO-OFDM chain dedicated to the transmission of scalable multimedia con-tent in a realistic context with the IEEE 802.1n standard. This study presents a new contribu-tion of the TR method evaluation to improve the visual quality of the JPWL transmitted imag-es, considering in the same time the multimedia content, the PA nonlinearity and the channel transmission distortions.

The aim of the dissertation was to design a realistic, low-complexity non-binary (NB) low density parity check (LDPC) coded space-time-frequency (STF) coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with an iterative joint decoder and detector structure at the receiver. The goal of the first part of the dissertation was to compare the performance of different design procedures for NB-LDPC codes on an additive white Gaussian noise (AWGN) channel, taking into account the constraint on the code length. The effect of quantisation on the performance of the code was also analysed. Different methods for choosing the NB elements in the parity check matrix were compared. For the STF coding, a class of universal STF codes was used. These codes use linear pre-coding and a layering approach based on Diophantine numbers to achieve full diversity and a transmission rate (in symbols per channel use per frequency) equal to the number of transmitter antennas. The study of the system considers a comparative performance analysis of di erent ST, SF and STF codes. The simulations of the system were performed on a triply selective block fading channel. Thus, there was selectivity in the fading over time, space and frequency. The effect of quantisation at the receiver on the achievable diversity of linearly pre-coded systems (such as the STF codes used) was mathematically derived and verified with simulations. A sphere decoder (SD) was used as a MIMO detector. The standard method used to create a soft-input soft output (SISO) SD uses a hard-to-soft process and the max-log-map approximation. A new approach was developed which combines a Hopfield network with the SD. This SD-Hopfield detector was connected with the fast Fourier transform belief propagation (FFT-BP) algorithm in an iterative structure. This iterative system was able to achieve the same bit error rate (BER) performance as the original SISO-SD at a reduced complexity. The use of the iterative Hopfield-SD and FFT-BP decoder system also allows performance to be traded off for complexity by varying the number of decoding iterations. The complete system employs a NB-LDPC code concatenated with an STF code at the transmitter with a SISO-SD and FFT-BP decoder connected in an iterative structure at the receiver. The system was analysed in varying channel conditions taking into account the effect of correlation and quantisation. The performance of different SF and STF codes were compared and analysed in the system. An analysis comparing different numbers of FFT-BP and outer iterations was also done. AFRIKAANS : Die doel van die verhandeling was om ’n realistiese, lae-kompleksiteit nie-binˆere (NB) LDPC gekodeerde ruimte-tyd-frekwensie-gekodeerde MIMO-OFDM-sisteem met iteratiewe gesamentlike dekodeerder- en detektorstrukture by die ontvanger te ontwerp. Die eerstem deel van die verhandeling was om die werkverrigting van verskillende ontwerpprosedures vir NB-LDPC kodes op ’n gesommeerde wit Gausruiskanaal te vergelyk met inagneming van die beperking op die lengte van die kode. Verskillende metodes om die nie-bineêre elemente in die pariteitstoetsmatriks te kies, is gebruik. Vir die ruimte-tyd-frekwensiekodering is ’n klas universele ruimte-tyd-frekwensiekodes gebruik. Hierdie kodes gebruik lineêre pre-kodering en ’n laagbenadering gebaseer op Diofantiese syfers om volle diversiteit te bereik en ’n oordragtempo (in simbole per kanaalgebruik per frekwensie) gelyk aan die aantal senderantennes. Die studie van die sisteem oorweeg ’n vergelykende werkverrigtinganalisie van verskillende ruimte-tyd-, ruimte-freksensie- en ruimte-tyd-frekwensiekodes. Die simulasies van die sisteem is gedoen op ’n drievoudig selektiewe blokwegsterwingskanaal. Daar was dus selektiwiteit in die wegsterwing oor tyd, ruimte en frekwensie. Die effek van kwantisering by die ontvanger op die bereikbare diversiteit van lineêr pre-gekodeerde sisteme (soos die ruimte-tyd-frekwensiekodes wat gebruik is) is matematies afgelei en bevestig deur simulasies. ’n Sfeerdekodeerder (SD) is gebruik as ’n MIMO-detektor. Die standaardmetode wat gebuik is om ’n sagte-inset-sagte-uitset (SISO) SD te skep, gebruik ’n harde-na-sagte proses en die maksimum logaritmiese afbeelding-benadering. ’n Nuwe benadering wat ’n Hopfield-netwerk met die SD kombineer, is ontwikkel. Hierdie SD-Hopfield-detektor is verbind met die FFT-BP-algoritme in iteratiewe strukture. Hierdie iteratiewe sisteem was in staat om dieselfde bisfouttempo te bereik as die oorspronklike SISO-SD, met laer kompleksiteit. Die gebruik van die iteratiewe Hopfield-SD en FFT-BP-dekodeerdersisteem maak ook daarvoor voorsiening dat werkverrigting opgeweeg kan word teen kompleksiteit deur die aantal dekodering-iterasies te varieer. Die volledige sisteem maak gebruik van ’n QC-NB-LDPC-kode wat met ’n ruimte-tyd-frekwensiekode by die sender aaneengeskakel is met ’n SISO-SD en FFT-BP-dekodeerder wat in ’n iteratiewe struktuur by die ontvanger gekoppel is. Die sisteem is onder ’n verskeidenheid kanaalkondisies ge-analiseer met inagneming van die effek van korrelasie en kwantisering. Die werkverrigting van verskillende ruimte-frekwensie- en ruimte-tyd-frekwensiekodes is vergelyk en in die sisteem ge-analiseer. ’n Analise om ’n wisselende aantal FFT-BP en buite-iterasies te vergelyk, is ook gedoen. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

The integration of Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) techniques has become a preferred solution for the high rate wireless technologies due to its high spectral efficiency, robustness to frequency selective fading, increased diversity gain, and enhanced system capacity. The main drawback of OFDM-based systems is their susceptibility to impairments such as Carrier Frequency Offset (CFO), Sampling Frequency Offset (SFO), Symbol Timing Error (STE), Phase Noise (PHN), and fading channel. These impairments, if not properly estimated and compensated, degrade the performance of the OFDM-based systems In this thesis, a system model for MIMO-OFDM that takes into account the effects of all these impairments is formulated. Using this system model, we de-rive Cramer-Rao Lower Bounds (CRLBs) for the joint estimation of deterministic impairments in MIMO-OFDM system, which show the coupling effect among different impairments and the significance of the joint estimation. Also, Bayesian CRLBs for the joint estimation of random impairments in OFDM system are derived. Similarly, we derive Hybrid CRLBs for the joint estimation of random and deterministic impairments in OFDM system, which show the significance of using Bayesian approach in estimation. Further, we investigate different algorithms for the joint estimation of all impairments in OFDM-based system. Maximum Likelihood (ML) algorithms and its low complexity variants, for the joint estimation of CFO, SFO, STE, and channel in MIMO-OFDM system, are proposed. We propose a low complexity ML algorithm which uses Compressed Sensing (CS) based channel estimation method in a sparse fading sce-nario, where the received samples used for estimation are less than that required for a Least Squares (LS) or Maximum a posteriori (MAP) based estimation. Also, we propose MAP algorithms for the joint estimation of the random impairments, PHN and channel, utilizing their statistical knowledge which is known a priori. Joint estimation algorithms for SFO and channel in OFDM system, using Bayesian framework, are also proposed in this thesis. The performance of the estimation methods is studied through simulations and numerical results show that the performance of the proposed algorithms is better than existing algorithms and is closer to the derived CRLBs.

This thesis considers the real-time implementation phases of a multiple-input multiple-output (MIMO) wireless communication system. The parts which are related to the implementation detail the blocks realized on a field programmable gate array (FPGA) board and define the connections between these blocks and typical radio frequency front-end modules assisting the wireless communication. Two sides of the implemented communication testbed are discussed separately as the transmitter and the receiver parts. In addition to usual building blocks of the transmitter and the receiver blocks, a special type of iterative parallelized decoding architecture has also been implemented on the testbed to demonstrate its potential in low-latency communication systems. In addition to practical aspects, this thesis also presents theoretical findings for an improved version of the built system using analytical tools and simulation results for possible extensions to orthogonal frequency division multiplexing (OFDM).