7 research outputs found
Recommended from our members
Ultra-wideband systems exploiting orthonormal waveforms
textElectrical and Computer Engineerin
Channel Prediction for Mobile MIMO Wireless Communication Systems
Temporal variation and frequency selectivity of wireless channels constitute
a major drawback to the attainment of high gains in capacity
and reliability offered by multiple antennas at the transmitter and receiver
of a mobile communication system. Limited feedback and adaptive transmission
schemes such as adaptive modulation and coding, antenna selection,
power allocation and scheduling have the potential to provide the platform
of attaining the high transmission rate, capacity and QoS requirements in
current and future wireless communication systems. Theses schemes require
both the transmitter and receiver to have accurate knowledge of Channel
State Information (CSI). In Time Division Duplex (TDD) systems, CSI at
the transmitter can be obtained using channel reciprocity. In Frequency Division
Duplex (FDD) systems, however, CSI is typically estimated at the
receiver and fed back to the transmitter via a low-rate feedback link. Due to
the inherent time delays in estimation, processing and feedback, the CSI obtained
from the receiver may become outdated before its actual usage at the
transmitter. This results in significant performance loss, especially in high
mobility environments. There is therefore a need to extrapolate the varying
channel into the future, far enough to account for the delay and mitigate the
performance degradation.
The research in this thesis investigates parametric modeling and prediction
of mobile MIMO channels for both narrowband and wideband systems.
The focus is on schemes that utilize the additional spatial information offered
by multiple sampling of the wave-field in multi-antenna systems to
aid channel prediction. The research has led to the development of several
algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient
methods for the extrapolation of narrowband MIMO channels are proposed.
Various extensions were also developed. These include methods for wideband
channels, transmission using polarized antenna arrays, and mobile-to-mobile
systems.
Performance bounds on the estimation and prediction error are vital when
evaluating channel estimation and prediction schemes. For this purpose, analytical
expressions for bound on the estimation and prediction of polarized
and non-polarized MIMO channels are derived. Using the vector formulation
of the Cramer Rao bound for function of parameters, readily interpretable
closed-form expressions for the prediction error bounds were found for cases
with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The
derived performance bounds are very simple and so provide insight into system
design.
The performance of the proposed algorithms was evaluated using standardized
channel models. The effects of the temporal variation of multipath
parameters on prediction is studied and methods for jointly tracking the
channel parameters are developed. The algorithms presented can be utilized
to enhance the performance of limited feedback and adaptive MIMO
transmission schemes
Contribution à la conception d'un système de radio impulsionnelle ultra large bande intelligent
Faced with an ever increasing demand of high data-rates and improved adaptability among existing systems, which inturn is resulting in spectrum scarcity, the development of new radio solutions becomes mandatory in order to answer the requirements of these emergent applications. Among the recent innovations in the field of wireless communications,ultra wideband (UWB) has generated significant interest. Impulse based UWB (IR-UWB) is one attractive way of realizing UWB systems, which is characterized by the transmission of sub nanoseconds UWB pulses, occupying a band width up to 7.5 GHz with extremely low power density. This large band width results in several captivating features such as low-complexity low-cost transceiver, ability to overlay existing narrowband systems, ample multipath diversity, and precise ranging at centimeter level due to extremely fine temporal resolution.In this PhD dissertation, we investigate some of the key elements in the realization of an intelligent time-hopping based IR-UWB system. Due to striking resemblance of IR-UWB inherent features with cognitive radio (CR) requirements, acognitive UWB based system is first studied. A CR in its simplest form can be described as a radio, which is aware ofits surroundings and adapts intelligently. As sensing the environment for the availability of resources and then consequently adapting radio’s internal parameters to exploit them opportunistically constitute the major blocks of any CR, we first focus on robust spectrum sensing algorithms and the design of adaptive UWB waveforms for realizing a cognitive UWB radio. The spectrum sensing module needs to function with minimum a-priori knowledge available about the operating characteristics and detect the primary users as quickly as possible. Keeping this in mind, we develop several spectrum sensing algorithms invoking recent results on the random matrix theory, which can provide efficient performance with a few number of samples. Next, we design the UWB waveform using a linear combination of Bsp lines with weight coefficients being optimized by genetic algorithms. This results in a UWB waveform that is spectrally efficient and at the same time adaptable to incorporate the cognitive radio requirements. In the 2nd part of this thesis, some research challenges related to signal processing in UWB systems, namely synchronization and dense multipath channel estimation are addressed. Several low-complexity non-data-aided (NDA) synchronization algorithms are proposed for BPSK and PSM modulations, exploiting either the orthogonality of UWB waveforms or theinherent cyclostationarity of IR-UWB signaling. Finally, we look into the channel estimation problem in UWB, whichis very demanding due to particular nature of UWB channels and at the same time very critical for the coherent Rake receivers. A method based on a joint maximum-likelihood (ML) and orthogonal subspace (OS) approaches is proposed which exhibits improved performance than both of these methods individually.Face à une demande sans cesse croissante de haut débit et d’adaptabilité des systèmes existants, qui à son tour se traduit par l’encombrement du spectre, le développement de nouvelles solutions dans le domaine des communications sans fil devient nécessaire afin de répondre aux exigences des applications émergentes. Parmi les innovations récentes dans ce domaine, l’ultra large bande (UWB) a suscité un vif intérêt. La radio impulsionnelle UWB (IR-UWB), qui est une solution intéressante pour réaliser des systèmes UWB, est caractérisée par la transmission des impulsions de très courte durée, occupant une largeur de bande allant jusqu’à 7,5 GHz, avec une densité spectrale de puissance extrêmement faible. Cette largeur de bande importante permet de réaliser plusieurs fonctionnalités intéressantes, telles que l’implémentation à faible complexité et à coût réduit, la possibilité de se superposer aux systèmes à bande étroite, la diversité spatiale et la localisation très précise de l’ordre centimétrique, en raison de la résolution temporelle très fine.Dans cette thèse, nous examinons certains éléments clés dans la réalisation d'un système IR-UWB intelligent. Nous avons tout d’abord proposé le concept de radio UWB cognitive à partir des similarités existantes entre l'IR-UWB et la radio cognitive. Dans sa définition la plus simple, un tel système est conscient de son environnement et s'y adapte intelligemment. Ainsi, nous avons tout d’abord focalisé notre recherché sur l’analyse de la disponibilité des ressources spectrales (spectrum sensing) et la conception d’une forme d’onde UWB adaptative, considérées comme deux étapes importantes dans la réalisation d'une radio cognitive UWB. Les algorithmes de spectrum sensing devraient fonctionner avec un minimum de connaissances a priori et détecter rapidement les utilisateurs primaires. Nous avons donc développé de tels algorithmes utilisant des résultats récents sur la théorie des matrices aléatoires, qui sont capables de fournir de bonnes performances, avec un petit nombre d'échantillons. Ensuite, nous avons proposé une méthode de conception de la forme d'onde UWB, vue comme une superposition de fonctions B-splines, dont les coefficients de pondération sont optimisés par des algorithmes génétiques. Il en résulte une forme d'onde UWB qui est spectralement efficace et peut s’adapter pour intégrer les contraintes liées à la radio cognitive. Dans la 2ème partie de cette thèse, nous nous sommes attaqués à deux autres problématiques importantes pour le fonctionnement des systèmes UWB, à savoir la synchronisation et l’estimation du canal UWB, qui est très dense en trajets multiples. Ainsi, nous avons proposé plusieurs algorithmes de synchronisation, de faible complexité et sans séquence d’apprentissage, pour les modulations BPSK et PSM, en exploitant l'orthogonalité des formes d'onde UWB ou la cyclostationnarité inhérente à la signalisation IR-UWB. Enfin, nous avons travaillé sur l'estimation du canal UWB, qui est un élément critique pour les récepteurs Rake cohérents. Ainsi, nous avons proposé une méthode d’estimation du canal basée sur une combinaison de deux approches complémentaires, le maximum de vraisemblance et la décomposition en sous-espaces orthogonaux,d’améliorer globalement les performances
Differentially-encoded di-symbol time-division multiuser impulse radio in UWB channel
Ph.DDOCTOR OF PHILOSOPH
Recommended from our members
Short-Range Millimeter-Wave Sensing and Imaging: Theory, Experiments and Super-Resolution Algorithms
Recent advancements in silicon technology offer the possibility of realizing low-cost and highly integrated radar sensor and imaging systems in mm-wave (between 30 and 300 GHz) and beyond. Such active short-range mm-wave systems have a wide range of applications including medical imaging, security scanning, autonomous vehicle navigation, and human gesture recognition. Moving to higher frequencies provides us with the spectral and spatial degrees of freedom that we need for high resolution imaging and sensing application. Increased bandwidth availability enhances range resolution by increasing the degrees of freedom in the time-frequency domain. Cross-range resolution is enhanced by the increase in the number of spatial degrees of freedom for a constrained form factor. The focus of this thesis is to explore system design and algorithmic development to utilize the available degrees of freedom in mm-wave frequencies in order to realize imaging and sensing capabilities under cost, complexity and form factor constraints. We first consider the fundamental problem of estimating frequencies and gains in a noisy mixture of sinusoids. This problem is ubiquitous in radar sensing applications, including target range and velocity estimation using standard radar waveforms (e.g., chirp or stepped frequency continuous wave), and direction of arrival estimation using an array of antenna elements. We have developed a fast and robust iterative algorithm for super-resolving the frequencies and gains, and have demonstrated near-optimal performance in terms of frequency estimation accuracy by benchmarking against the Cramer Rao Bound in various scenarios.Next, we explore cross-range radar imaging using an array of antenna elements under severe cost, complexity and form factor constraints. We show that we must account for such constraints in a manner that is quite different from that of conventional radar, and introduce new models and algorithms validated by experimental results. In order to relax the synchronization requirements across multiple transceiver elements we have considered the monostatic architecture in which only the co-located elements are synchronized. We investigate the impact of sparse spatial sampling by reducing the number of array antenna elements, and show that ``sparse monostatic'' architecture leads to grating lobe artifact, which introduces ambiguity in the detection/estimation of point targets in the scene. At short ranges, however, targets are ``low-pass'' and contain extended features (consisting of a continuum of points), and are not well-modeled by a small number of point scatterers. We introduce the concept of ``spatial aggregation,'' which provides the flexibility of constructing a dictionary in which each atom corresponds to a collection of point scatterers, and demonstrate its effectiveness in suppressing the grating lobes and preserving the information in the scene.Finally, we take a more fundamental and systematic approach based on singular decomposition of the imaging system, to understand the information capacity and the limits of performance for various geometries. In general, a scene can be described by an infinite number of independent parameters. However, the number of independent parameters that can be measured through an imaging system (also known as the degrees of freedom of the system) is typically finite, and is constrained by the geometry and wavelength. We introduce a measure to predict the number of spatial degrees of freedom of 1D imaging systems for both monostatic and multistatic array architectures. Our analysis reveals that there is no fundamental benefit in multistatic architecture compared to monostatic in terms of achievable degrees of freedom. The real benefit of multistatic architecture from a practical point of view, is in being able to design sparse transmit and receive antenna arrays that are capable of achieving the available degrees of freedom. Moreover, our analytical framework opens up new avenues to investigate image formation techniques that aim to reconstruct the reflectivity function of the scene by solving an inverse scattering problem, and provides crucial insights on the achievable resolution