Multiuser detection employing recurrent neural networks for DS-CDMA systems.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2006.Over the last decade, access to personal wireless communication networks has evolved to a point of necessity. Attached to the phenomenal growth of the telecommunications industry in recent times is an escalating demand for higher data rates and efficient spectrum utilization. This demand is fuelling the advancement of third generation (3G), as well as future, wireless networks. Current 3G technologies are adding a dimension of mobility to services that have become an integral part of modem everyday life. Wideband code division multiple access (WCDMA) is the standardized multiple access scheme for 3G Universal Mobile Telecommunication System (UMTS). As an air interface solution, CDMA has received considerable interest over the past two decades and a great deal of current research is concerned with improving the application of CDMA in 3G systems. A factoring component of CDMA is multiuser detection (MUD), which is aimed at enhancing system capacity and performance, by optimally demodulating multiple interfering signals that overlap in time and frequency. This is a major research problem in multipoint-to-point communications. Due to the complexity associated with optimal maximum likelihood detection, many different sub-optimal solutions have been proposed. This focus of this dissertation is the application of neural networks for MUD, in a direct sequence CDMA (DS-CDMA) system. Specifically, it explores how the Hopfield recurrent neural network (RNN) can be employed to give yet another suboptimal solution to the optimization problem of MUD. There is great scope for neural networks in fields encompassing communications. This is primarily attributed to their non-linearity, adaptivity and key function as data classifiers. In the context of optimum multiuser detection, neural networks have been successfully employed to solve similar combinatorial optimization problems. The concepts of CDMA and MUD are discussed. The use of a vector-valued transmission model for DS-CDMA is illustrated, and common linear sub-optimal MUD schemes, as well as the maximum likelihood criterion, are reviewed. The performance of these sub-optimal MUD schemes is demonstrated. The Hopfield neural network (HNN) for combinatorial optimization is discussed. Basic concepts and techniques related to the field of statistical mechanics are introduced and it is shown how they may be employed to analyze neural classification. Stochastic techniques are considered in the context of improving the performance of the HNN. A neural-based receiver, which employs a stochastic HNN and a simulated annealing technique, is proposed. Its performance is analyzed in a communication channel that is affected by additive white Gaussian noise (AWGN) by way of simulation. The performance of the proposed scheme is compared to that of the single-user matched filter, linear decorrelating and minimum mean-square error detectors, as well as the classical HNN and the stochastic Hopfield network (SHN) detectors. Concluding, the feasibility of neural networks (in this case the HNN) for MUD in a DS-CDMA system is explored by quantifying the relative performance of the proposed model using simulation results and in view of implementation issues

Code-division multiplexing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 395-404).(cont.) counterpart. Among intra-cell orthogonal schemes, we show that the most efficient broadcast signal is a linear superposition of many binary orthogonal waveforms. The information set is also binary. Each orthogonal waveform is generated by modulating a periodic stream of finite-length chip pulses with a receiver-specific signature code that is derived from a special class of binary antipodal, superimposed recursive orthogonal code sequences. With the imposition of practical pulse shapes for carrier modulation, we show that multi-carrier format using cosine functions has higher bandwidth efficiency than the single-carrier format, even in an ideal Gaussian channel model. Each pulse is shaped via a prototype baseband filter such that when the demodulated signal is detected through a baseband matched filter, the resulting output samples satisfy the Generalized Nyquist criterion. Specifically, we propose finite-length, time overlapping orthogonal pulse shapes that are g-Nyquist. They are derived from extended and modulated lapped transforms by proving the equivalence between Perfect Reconstruction and Generalized Nyquist criteria. Using binary data modulation format, we measure and analyze the accuracy of various Gaussian approximation methods for spread-spectrum modulated (SSM) signalling ...We study forward link performance of a multi-user cellular wireless network. In our proposed cellular broadcast model, the receiver population is partitioned into smaller mutually exclusive subsets called cells. In each cell an autonomous transmitter with average transmit power constraint communicates to all receivers in its cell by broadcasting. The broadcast signal is a multiplex of independent information from many remotely located sources. Each receiver extracts its desired information from the composite signal, which consists of a distorted version of the desired signal, interference from neighboring cells and additive white Gaussian noise. Waveform distortion is caused by time and frequency selective linear time-variant channel that exists between every transmitter-receiver pair. Under such system and design constraints, and a fixed bandwidth for the entire network, we show that the most efficient resource allocation policy for each transmitter based on information theoretic measures such as channel capacity, simultaneously achievable rate regions and sum-rate is superposition coding with successive interference cancellation. The optimal policy dominates over its sub-optimal alternatives at the boundaries of the capacity region. By taking into account practical constraints such as finite constellation sets, frequency translation via carrier modulation, pulse shaping and real-time signal processing and decoding of finite-length waveforms and fairness in rate distribution, we argue that sub-optimal orthogonal policies are preferred. For intra-cell multiplexing, all orthogonal schemes based on frequency, time and code division are equivalent. For inter-cell multiplexing, non-orthogonal code-division has a larger capacity than its orthogonalby Ceilidh Hoffmann.Ph.D

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Digital Signal Processing (Second Edition)

This book provides an account of the mathematical background, computational methods and software engineering associated with digital signal processing. The aim has been to provide the reader with the mathematical methods required for signal analysis which are then used to develop models and algorithms for processing digital signals and finally to encourage the reader to design software solutions for Digital Signal Processing (DSP). In this way, the reader is invited to develop a small DSP library that can then be expanded further with a focus on his/her research interests and applications. There are of course many excellent books and software systems available on this subject area. However, in many of these publications, the relationship between the mathematical methods associated with signal analysis and the software available for processing data is not always clear. Either the publications concentrate on mathematical aspects that are not focused on practical programming solutions or elaborate on the software development of solutions in terms of working ‘black-boxes’ without covering the mathematical background and analysis associated with the design of these software solutions. Thus, this book has been written with the aim of giving the reader a technical overview of the mathematics and software associated with the ‘art’ of developing numerical algorithms and designing software solutions for DSP, all of which is built on firm mathematical foundations. For this reason, the work is, by necessity, rather lengthy and covers a wide range of subjects compounded in four principal parts. Part I provides the mathematical background for the analysis of signals, Part II considers the computational techniques (principally those associated with linear algebra and the linear eigenvalue problem) required for array processing and associated analysis (error analysis for example). Part III introduces the reader to the essential elements of software engineering using the C programming language, tailored to those features that are used for developing C functions or modules for building a DSP library. The material associated with parts I, II and III is then used to build up a DSP system by defining a number of ‘problems’ and then addressing the solutions in terms of presenting an appropriate mathematical model, undertaking the necessary analysis, developing an appropriate algorithm and then coding the solution in C. This material forms the basis for part IV of this work. In most chapters, a series of tutorial problems is given for the reader to attempt with answers provided in Appendix A. These problems include theoretical, computational and programming exercises. Part II of this work is relatively long and arguably contains too much material on the computational methods for linear algebra. However, this material and the complementary material on vector and matrix norms forms the computational basis for many methods of digital signal processing. Moreover, this important and widely researched subject area forms the foundations, not only of digital signal processing and control engineering for example, but also of numerical analysis in general. The material presented in this book is based on the lecture notes and supplementary material developed by the author for an advanced Masters course ‘Digital Signal Processing’ which was first established at Cranfield University, Bedford in 1990 and modified when the author moved to De Montfort University, Leicester in 1994. The programmes are still operating at these universities and the material has been used by some 700++ graduates since its establishment and development in the early 1990s. The material was enhanced and developed further when the author moved to the Department of Electronic and Electrical Engineering at Loughborough University in 2003 and now forms part of the Department’s post-graduate programmes in Communication Systems Engineering. The original Masters programme included a taught component covering a period of six months based on two semesters, each Semester being composed of four modules. The material in this work covers the first Semester and its four parts reflect the four modules delivered. The material delivered in the second Semester is published as a companion volume to this work entitled Digital Image Processing, Horwood Publishing, 2005 which covers the mathematical modelling of imaging systems and the techniques that have been developed to process and analyse the data such systems provide. Since the publication of the first edition of this work in 2003, a number of minor changes and some additions have been made. The material on programming and software engineering in Chapters 11 and 12 has been extended. This includes some additions and further solved and supplementary questions which are included throughout the text. Nevertheless, it is worth pointing out, that while every effort has been made by the author and publisher to provide a work that is error free, it is inevitable that typing errors and various ‘bugs’ will occur. If so, and in particular, if the reader starts to suffer from a lack of comprehension over certain aspects of the material (due to errors or otherwise) then he/she should not assume that there is something wrong with themselves, but with the author

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Lasers à blocage de modes à base de boîtes et bâtonnets quantiques pour les peignes de fréquences optiques

Optical frequency combs, generating tens of equally spaced optical carriers from a single laser source, are very attractive for next-generation wavelength division multiplexing (WDM) communication systems. This PhD thesis presents a study on the optical frequency combs generated by mode-locked laser diodes based on low-dimensional semiconductor nanostructures. In this work, the mode-locking performances of single-section Fabry-Pérot lasers based on different material systems are compared on the basis of the optical spectrum width, the timing jitter and pulse generation capabilities. Then, noticing that InAs quantum dashes grown on InP exhibit on average better characteristics than other examined materials, their unique properties in terms of comb stability and pulse chirp are studied in more detail. Laser chirp, in particular, is first investigated by frequency resolved optical gating (FROG) characterizations. Then, chromatic dispersion of the laser material is assessed in order to verify whether it can account for the large chirp values measured by FROG. For that, a high sensitivity optical frequency-domain reflectometry setup is used and its measurement capabilities are extensively studied and validated. Finally, the combs generated by quantum dash mode-locked lasers are successfully employed for high data rate transmissions using direct-detection optical orthogonal frequency division multiplexing. Terabit per second capacities, as well as the low cost of this system architecture, appear to be particularly promising for future datacom applicationsLes peignes de longueurs d'onde, produisant des dizaines de porteuses optiques régulièrement espacées à partir d'une seule source laser, présentent un grand intérêt pour les systèmes de communication à haut débit. Ce travail de thèse porte sur les peignes générés par les diodes laser à blocage de modes basées sur des nanostructures semi-conductrices à basse dimensionnalité. Dans cette étude, les performances en verrouillage de modes de lasers Fabry-Pérot mono-section basés sur différents systèmes de matériaux sont comparées sur la base de la largeur du spectre optique d'émission et de la capacité à produire des impulsions courtes à faible gigue temporelle. En remarquant que les lasers à base de bâtonnets quantiques InAs sur InP présentent de meilleures caractéristiques par rapport aux autres matériaux examinés, leurs propriétés spécifiques en termes de stabilité des peignes de fréquences optiques et de chirp des impulsions sont étudiées plus en détail. Le chirp est d'abord étudié par la technique FROG (frequency-resolved optical gating). Ensuite, la dispersion chromatique du matériau laser est évaluée afin de vérifier si elle peut expliquer les grandes valeurs de chirp mesurées par FROG. Pour cela la technique de réflectométrie optique dans le domaine fréquentiel est utilisée et ses capacités uniques de mesure ont été étudiées et validées. Enfin, ces lasers sont employés avec succès pour les transmissions haut débit à l'aide de la technique de modulation optique OFDM (orthogonal frequency-division multiplexing) en détection directe. Débits de l'ordre du térabit par seconde, ainsi que le faible coût de l’architecture du système, sont très prometteurs pour les data center

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected