MIMO Systems: Principles, Iterative Techniques, and advanced Polarization

International audienceThis chapter considers the principles of multiple-input multiple-output (MIMO) wireless communication systems as well as some recent accomplishments concerning their implementation. By employing multiple antennas at both transmitter and receiver, very high data rates can be achieved under the condition of deployment in a rich-scattering propagation medium. This interesting property of MIMO systems suggests their use in the future high-rate and high-quality wireless communication systems. Several concepts in MIMO systems are reviewed in this chapter. We first consider MIMO channel models and recall the basic principles of MIMO structures and channel modeling. We next study the MIMO channel capacity and present the early developments in these systems concerning the information theory aspect. Iterative signal detection is considered next; it considers iterative techniques for space-time decoding. As the capacity is inversely proportional to the spatial channel correlation, MIMO antennas should be sufficiently separated, usually by several wavelengths. In order to minimize antennas' deployment, we present advanced polarization diversity techniques for MIMO systems and explain how they can help to reduce the spatial correlation in order to achieve high transmission rates. We end the chapter by considering the application of MIMO systems in local area networks, as well as their potential in enhancing range, localization, and power efficiency of sensor networks

Advanced MIMO Techniques: Polarization Diversity and Antenna Selection

International audienceThis chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance

A novel indoor localization scheme based on fingerprinting technique And CDMA signals

International audience—In this paper, we propose a novel acoustic local-ization system based on fingerprinting technique. It deploys the Time Of Arrival of CDMA signals emitted by speakers to locate a microphone. The system is inspired from our earlier proposed scheme [1] which deploys the lateration method. Here, we adopt the fingerprinting technique since it is more applicable to indoor environments. The position estimation is accomplished through nonparametric kernel regression. Performance are evaluated by experiments, performed in a hall of interns in National School of Engineers of Le Mans. Results have shown that our proposed scheme provides an accuracy of 8.5 cm within 80% precision

Multi-Dimensional Codebooks for Multiple Access Schemes

The sparse code multiple access (SCMA) scheme directly maps the incoming bits of several sources (users/streams) to complex multi-dimensional codewords selected from a specific predefined sparse codebook set. The codewords of all sources are then superimposed and exchanged. The shaping gain of the multi-dimensional constellation of SCMA leads to a better system performance. The decoder’s objective will be to separate the superimposed sparse codewords. Most existing works on SCMA decoders employ message passing algorithm (MPA) or one of its variations, or a combination of MPA and other methods. The system architecture is highlighted and its basic principles are presented. Then, an overview of main multi-dimensional constellations for SCMA systems will be provided. Afterwards, we will focus on how the SCMA codebooks are decoded and how their performance is evaluated and compared

Synchronisation conjointe rythme et phase en CDMA optimisée dans un contexte multi-utilisateur

- Ce papier traîte du problème de la synchronisation conjointe rythme et phase pour des signaux CDMA en environnement multi-utilisateur. Nous proposons une nouvelle version d'un algorithme standard de synchronisation optimisé pour un contexte multi-utilisateur. L'amélioration va porter sur la boucle la plus sensible aux interférences, la boucle de récupération de rythme. L'optimisation consiste à insérer un préfiltre dans cette boucle et à calculer ses coefficients de manière à minimiser la variance du retard estimé. La résolution de ce problème de minimisation avec la méthode des multiplicateurs de Lagrange conduit à une expression analytique des coefficients du préfiltre. Les performances de l'algorithme optimisé sont ensuite mises en évidence à l'aide d'une simulation. Le principal résultat de cette analyse est qu'un préfiltre même de petite taille permet d'augmenter les performances de la récupération de rythme, mais aussi de la récupération de phase du fait de l'interaction de ces deux tâches

Automatic Speech Emotion Recognition Using Machine Learning

This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS