Cooperative Lattice Coding and Decoding

A novel lattice coding framework is proposed for outage-limited cooperative channels. This framework provides practical implementations for the optimal cooperation protocols proposed by Azarian et al. In particular, for the relay channel we implement a variant of the dynamic decode and forward protocol, which uses orthogonal constellations to reduce the channel seen by the destination to a single-input single-output time-selective one, while inheriting the same diversity-multiplexing tradeoff. This simplification allows for building the receiver using traditional belief propagation or tree search architectures. Our framework also generalizes the coding scheme of Yang and Belfiore in the context of amplify and forward cooperation. For the cooperative multiple access channel, a tree coding approach, matched to the optimal linear cooperation protocol of Azarain et al, is developed. For this scenario, the MMSE-DFE Fano decoder is shown to enjoy an excellent tradeoff between performance and complexity. Finally, the utility of the proposed schemes is established via a comprehensive simulation study.Comment: 25 pages, 8 figure

Design, analysis and evaluation of sigma-delta based beamformers for medical ultrasound imaging applications

The inherent analogue nature of medical ultrasound signals in conjunction with the abundant merits provided by digital image acquisition, together with the increasing use of relatively simple front-end circuitries, have created considerable demand for single-bit beamformers in digital ultrasound imaging systems. Furthermore, the increasing need to design lightweight ultrasound systems with low power consumption and low noise, provide ample justification for development and innovation in the use of single-bit beamformers in ultrasound imaging systems. The overall aim of this research program is to investigate, establish, develop and confirm through a combination of theoretical analysis and detailed simulations, that utilize raw phantom data sets, suitable techniques for the design of simple-to-implement hardware efficient digital ultrasound beamformers to address the requirements for 3D scanners with large channel counts, as well as portable and lightweight ultrasound scanners for point-of-care applications and intravascular imaging systems. In addition, the stability boundaries of higher-order High-Pass (HP) and Band-Pass (BP) Σ−Δ modulators for single- and dual- sinusoidal inputs are determined using quasi-linear modeling together with the describing-function method, to more accurately model the modulator quantizer. The theoretical results are shown to be in good agreement with the simulation results for a variety of input amplitudes, bandwidths, and modulator orders. The proposed mathematical models of the quantizer will immensely help speed up the design of higher order HP and BP Σ−Δ modulators to be applicable for digital ultrasound beamformers. Finally, a user friendly design and performance evaluation tool for LP, BP and HP modulators is developed. This toolbox, which uses various design methodologies and covers an assortment of modulators topologies, is intended to accelerate the design process and evaluation of modulators. This design tool is further developed to enable the design, analysis and evaluation of beamformer structures including the noise analyses of the final B-scan images. Thus, this tool will allow researchers and practitioners to design and verify different reconstruction filters and analyze the results directly on the B-scan ultrasound images thereby saving considerable time and effort

New strategies for low noise, agile PLL frequency synthesis

Phase-Locked Loop based frequency synthesis is an essential technique employed in wireless communication systems for local oscillator generation. The ultimate goal in any design of frequency synthesisers is to generate precise and stable output frequencies with fast switching and minimal spurious and phase noise. The conflict between high resolution and fast switching leads to two separate integer synthesisers to satisfy critical system requirements. This thesis concerns a new sigma-delta fractional-N synthesiser design which is able to be directly modulated at high data rates while simultaneously achieving good noise performance. Measured results from a prototype indicate that fast switching, low noise and spurious free spectra are achieved for most covered frequencies. The phase noise of the unmodulated synthesiser was measured −113 dBc/Hz at 100 kHz offset from the carrier. The intermodulation effect in synthesisers is capable of producing a family of spurious components of identical form to fractional spurs caused in quantisation process. This effect directly introduces high spurs on some channels of the synthesiser output. Numerical and analytic results describing this effect are presented and amplitude and distribution of the resulting fractional spurs are predicted and validated against simulated and measured results. Finally an experimental arrangement, based on a phase compensation technique, is presented demonstrating significant suppression of intermodulation-borne spurs. A new technique, pre-distortion noise shaping, is proposed to dramatically reduce the impact of fractional spurs in fractional-N synthesisers. The key innovation is the introduction in the bitstream generation process of carefully-chosen set of components at identical offset frequencies and amplitudes and in anti-phase with the principal fractional spurs. These signals are used to modify the Σ-Δ noise shaping, so that fractional spurs are effectively cancelled. This approach can be highly effective in improving spectral purity and reduction of spurious components caused by the Σ-Δ modulator, quantisation noise, intermodulation effects and any other circuit factors. The spur cancellation is achieved in the digital part of the synthesiser without introducing additional circuitry. This technique has been convincingly demonstrated by simulated and experimental results

Neural networks for optical channel equalization in high speed communication systems

La demande future de bande passante pour les données dépassera les capacités des systèmes de communication optique actuels, qui approchent de leurs limites en raison des limitations de la bande passante électrique des composants de l’émetteur. L’interférence intersymbole (ISI) due à cette limitation de bande est le principal facteur de dégradation pour atteindre des débits de données élevés. Dans ce mémoire, nous étudions plusieurs techniques de réseaux neuronaux (NN) pour combattre les limites physiques des composants de l’émetteur pilotés à des débits de données élevés et exploitant les formats de modulation avancés avec une détection cohérente. Notre objectif principal avec les NN comme égaliseurs de canaux ISI est de surmonter les limites des récepteurs optimaux conventionnels, en fournissant une complexité évolutive moindre et une solution quasi optimale. Nous proposons une nouvelle architecture bidirectionnelle profonde de mémoire à long terme (BiLSTM), qui est efficace pour atténuer les graves problèmes d’ISI causés par les composants à bande limitée. Pour la première fois, nous démontrons par simulation que notre BiLSTM profonde proposée atteint le même taux d’erreur sur les bits(TEB) qu’un estimateur de séquence à maximum de vraisemblance (MLSE) optimal pour la modulation MDPQ. Les NN étant des modèles pilotés par les données, leurs performances dépendent fortement de la qualité des données d’entrée. Nous démontrons comment les performances du BiLSTM profond réalisable se dégradent avec l’augmentation de l’ordre de modulation. Nous examinons également l’impact de la sévérité de l’ISI et de la longueur de la mémoire du canal sur les performances de la BiLSTM profonde. Nous étudions les performances de divers canaux synthétiques à bande limitée ainsi qu’un canal optique mesuré à 100 Gbaud en utilisant un modulateur photonique au silicium (SiP) de 35 GHz. La gravité ISI de ces canaux est quantifiée grâce à une nouvelle vue graphique des performances basée sur les écarts de performance de base entre les solutions optimales linéaires et non linéaires classiques. Aux ordres QAM supérieurs à la QPSK, nous quantifions l’écart de performance BiLSTM profond par rapport à la MLSE optimale à mesure que la sévérité ISI augmente. Alors qu’elle s’approche des performances optimales de la MLSE à 8QAM et 16QAM avec une pénalité, elle est capable de dépasser largement la solution optimale linéaire à 32QAM. Plus important encore, l’avantage de l’utilisation de modèles d’auto-apprentissage comme les NN est leur capacité à apprendre le canal pendant la formation, alors que la MLSE optimale nécessite des informations précises sur l’état du canal.The future demand for the data bandwidth will surpass the capabilities of current optical communication systems, which are approaching their limits due to the electrical bandwidth limitations of the transmitter components. Inter-symbol interference (ISI) due to this band limitation is the major degradation factor to achieve high data rates. In this thesis, we investigate several neural network (NN) techniques to combat the physical limits of the transmitter components driven at high data rates and exploiting the advanced modulation formats with coherent detection. Our main focus with NNs as ISI channel equalizers is to overcome the limitations of conventional optimal receivers, by providing lower scalable complexity and near optimal solution. We propose a novel deep bidirectional long short-term memory (BiLSTM) architecture, that is effective in mitigating severe ISI caused by bandlimited components. For the first time, we demonstrate via simulation that our proposed deep BiLSTM achieves the same bit error rate (BER) performance as an optimal maximum likelihood sequence estimator (MLSE) for QPSK modulation. The NNs being data-driven models, their performance acutely depends on input data quality. We demonstrate how the achievable deep BiLSTM performance degrades with the increase in modulation order. We also examine the impact of ISI severity and channel memory length on deep BiLSTM performance. We investigate the performances of various synthetic band-limited channels along with a measured optical channel at 100 Gbaud using a 35 GHz silicon photonic(SiP) modulator. The ISI severity of these channels is quantified with a new graphical view of performance based on the baseline performance gaps between conventional linear and nonlinear optimal solutions. At QAM orders above QPSK, we quantify deep BiLSTM performance deviation from the optimal MLSE as ISI severity increases. While deep BiLSTM approaches the optimal MLSE performance at 8QAM and 16QAM with a penalty, it is able to greatly surpass the linear optimal solution at 32QAM. More importantly, the advantage of using self learning models like NNs is their ability to learn the channel during the training, while the optimal MLSE requires accurate channel state information

Hardware reduction in digital delta-sigma modulators via error masking - part I: MASH DDSM

Two classes of techniques have been developed to whiten the quantization noise in digital delta-sigma modulators (DDSMs): deterministic and stochastic. In this two-part paper, a design methodology for reduced-complexity DDSMs is presented. The design methodology is based on error masking. Rules for selecting the word lengths of the stages in multistage architectures are presented. We show that the hardware requirement can be reduced by up to 20% compared with a conventional design, without sacrificing performance. Simulation and experimental results confirm theoretical predictions. Part I addresses MultistAge noise SHaping (MASH) DDSMs; Part II focuses on single-quantizer DDSMs.

Arquiteturas paralelas avançadas para transmissores 5G totalmente digitais

The fifth generation of mobile communications (5G) is being prepared and should be rolled out in the early coming years. Massive number of Radio-Frequency (RF) front-ends, peak data rates of 10 Gbps (everywhere and everytime), latencies lower than 10 msec and huge device densities are some of the expected disruptive capabilities. At the same time, previous generations can not be jeopardized, fostering the design of novel flexible and highly integrated radio transceivers able to support the simultaneous transmission of multi-band and multi-standard signals. The concept of all-digital transmission is being pointed out as a promising architecture to cope with such challenging requirements, due to its fully digital radio datapath. This thesis is focused on the proposal and validation of fully integrated and advanced digital transmitter architectures that excel the state-of-the-art in different figures of merit, such as transmission bandwidth, spectral purity, carrier agility, flexibility, and multi-band capability. The first part of this thesis introduces the concept of all-digital RF transmission. In particular, the foundations inherent to this thematic line are given, together with the recent advances reported in the state-of-the-art architectures.The core of this thesis, containing the main developments achieved during the Ph.D. work, is then presented and discussed. The first key contribution to the state-of-the-art is the use of cascaded Delta-Sigma (∆Σ) architectures to relax the analog filtering requirements of the conventional All-Digital Transmitters while maintaining the constant envelope waveform. Then, it is presented the first reported architecture where Antenna Arrays are directly driven by single-chip and single-bit All-Digital Transmitters, with promising results in terms of simplification of the RF front-ends and overall flexibility. Subsequently, the thesis proposes the first reported RF-stage All-Digital Transmitter that can be embedded within a single Field-Programmable Gate Array (FPGA) device. Thereupon, novel techniques to enable the design of wideband All-Digital Transmitters are reported. Finally, the design of concurrent multi-band transmitters is introduced. In particular, the design of agile and flexible dual and triple bands All-DigitalTransmitter (ADT) is demonstrated, which is a very important topic for scenarios that demand carrier aggregation. This Ph.D. contributes withseveral advances to the state-of-the-art of RF all-digital transmitters.A quinta geração de comunicações móveis (5G) está a ser preparada e deve ser comercializada nos próximos anos. Algumas das caracterı́sticas inovadoras esperadas passam pelo uso de um número massivo de font-ends de Rádio-Frequência (RF), taxas de pico de transmissão de dados de 10 Gbps (em todos os lugares e em todas as ocasiões), latências inferiores a 10 mseg e elevadas densidades de dispositivos. Ao mesmo tempo, as gerações anteriores não podem ser ignoradas, fomentando o design de novos transceptores de rádio flexı́veis e altamente integrados, capazes de suportar a transmissão simultânea de sinais multi-banda e multi-standard. O conceito de transmissão totalmente digital é considerado como um tipo de arquitetura promissora para lidar com esses requisitos desafiantes, devido ao seu datapath de rádio totalmente digital. Esta tese é focada na proposta e validação de arquiteturas de transmissores digitais totalmente integradas e avançadas que ultrapassam o estado da arte em diferentes figuras de mérito, como largura de banda de transmissão, pureza espectral, agilidade de portadora, flexibilidade e capacidade multibanda. A primeira parte desta tese introduz o conceito de transmissores de RF totalmente digitais. Em particular, os fundamentos inerentes a esta linha temática são apresentados, juntamente com os avanços mais recentes do estado-da-arte. O núcleo desta tese, contendo os principais desenvolvimentos alcançados durante o trabalho de doutoramento, é então apresentado e discutido. A primeira contribuição fundamental para o estado da arte é o uso de arquiteturas em cascata com moduladores ∆Σ para relaxar os requisitos de filtragem analógica dos transmissores RF totalmente digitais convencionais, mantendo a forma de onda envolvente constante. Em seguida, é apresentada a primeira arquitetura em que agregados de antenas são excitados diretamente por transmissores digitais de um único bit inseridos num único chip, com resultados promissores em termos de simplificação dos front-ends de RF e flexibilidade em geral. Posteriormente, é proposto o primeiro transmissor totalmente digital RF-stage relatado que pode ser incorporado dentro de um único Agregado de Células Lógicas Programáveis. Novas técnicas para permitir o desenho de transmissores RF totalmente digitais de banda larga são também apresentadas. Finalmente, o desenho de transmissores simultâneos de múltiplas bandas é exposto. Em particular, é demonstrado o desenho de transmissores de duas e três bandas ágeis e flexı́veis, que é um tópico essencial para cenários que exigem agregação de múltiplas bandas.Apoio financeiro da Fundação para a Ciência e Tecnologia (FCT) no âmbito de uma bolsa de doutoramento, ref. PD/BD/105857/2014.Programa Doutoral em Telecomunicaçõe

High efficiency delta-sigma modulation data converters

Enabled by continued device scaling in CMOS technology, more and more functions that were previously realized in separate chips are getting integrated on a single chip nowadays. Integration on silicon has opened the door to new portable wireless applications, and initiated a widespread use of these devices in our common everyday life. Wide signal bandwidth, high linearity and dynamic range, and low power dissipation are required of embedded data converters that are the performance-limiting key building blocks of those systems. Thus, power-efficient and highly-linear data conversion over wide range of signal bands is essential to get the full benefits from device scaling. This continued trend keeps innovation in the design of data converter continuing. Traditionally, delta-sigma modulation data converters proved to be very effective in applications where high resolution was necessary in a relatively narrow signal band. There have been active research efforts across academia and industry on the extension of achievable signal bandwidth without compromising the performance of these data converters. In this dissertation, architectural innovations, combined with effective design techniques for delta-sigma modulation data converters, are presented to overcome the associated limitations. The effectiveness of the proposed approaches is demonstrated by test results for the following state-of-the-art prototype designs: (1) a 0.8 V, 2.6 mW, 88 dB dual-channel audio delta-sigma modulation D/A converter with headphone driver; (2) an 88 dB ring-coupled delta-sigma ADC with 1.9 MHz bandwidth and -102.4 dB THD; (3) a multi-cell noise-coupled delta-sigma ADC with 1.9 MHz bandwidth, 88 dB DR, and -98 dB THD; (4) an 8.1 mW, 82 dB self-coupled delta-sigma ADC with 1.9 MHz bandwidth and -97 dB THD; (5) a noise-coupled time-interleaved delta-sigma ADC with 4.2 MHz bandwidth, -98 dB THD, and 79 dB SNDR; (6) a noise-coupled time-interleaved delta-sigma ADC with 2.5 MHz bandwidth, -104 dB THD, and 81 dB SNDR. As an extension of this research, two novel architectures for efficient double-sampling delta-sigma ADCs and improved low-distortion delta-sigma ADC are proposed, and validated by extensive simulations.Keywords: improved low-distortion modulator, time interleaving, data converter, multi-cell ADC, efficient double sampling, noise coupling, delta-sigma modulatio

Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces

Models of atypical development must also be models of normal development

Functional magnetic resonance imaging studies of developmental disorders and normal cognition that include children are becoming increasingly common and represent part of a newly expanding field of developmental cognitive neuroscience. These studies have illustrated the importance of the process of development in understanding brain mechanisms underlying cognition and including children ill the study of the etiology of developmental disorders

Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling

It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based on a review of connectionist models of acquired and developmental disorders in the domains of reading and past tense, as well as on new simulations, we explore the computational viability of Residual Normality and the potential role of development in producing behavioural deficits. Simulations demonstrate that damage to a developmental model can produce very different effects depending on whether it occurs prior to or following the training process. Because developmental disorders typically involve damage prior to learning, we conclude that the developmental process is a key component of the explanation of endstate impairments in such disorders. Further simulations demonstrate that in simple connectionist learning systems, the assumption of Residual Normality is undermined by processes of compensation or alteration elsewhere in the system. We outline the precise computational conditions required for Residual Normality to hold in development, and suggest that in many cases it is an unlikely hypothesis. We conclude that in developmental disorders, inferences from behavioural deficits to underlying structure crucially depend on developmental conditions, and that the process of ontogenetic development cannot be ignored in constructing models of developmental disorders