A Vector Channel Based Approach to MIMO Radar Waveform Design for Extended Targets

Radar systems have been used for many years for estimating, detecting, classifying, and imaging objects of interest (targets). Stealthier targets and more cluttered environments have created a need for more sophisticated radar systems to gain more precise information about the radar environment. Because modern radar systems are largely defined in software, adaptive radar systems have emerged that tailor system parameters such as the transmitted waveform and receiver filter to the target and environment in order to address this need. The basic structure of a radar system exhibits many similarities to the structure of a communication system. Recognizing the parallel composition of radar systems and information transmission systems, initial works have begun to explore the application of information theory to radar system design, but a great deal of work still remains to make a full and clear connection between the problems addressed by radar systems and communication systems. Forming a comprehensive definition of this connection between radar systems and information transmission systems and associated problem descriptions could facilitate the cross-discipline transfer of ideas and accelerate the development and improvement of new system design solutions in both fields. In particular, adaptive radar system design is a relatively new field which stands to benefit from the maturity of information theory developed for information transmission if a parallel can be drawn to clearly relate similar radar and communication problems. No known previous work has yet drawn a clear parallel between the general multiple-input multiple-output (MIMO) radar system model considering both the detection and estimation of multiple extended targets and a similar multiuser vector channel information transmission system model. The goal of this dissertation is to develop a novel vector channel framework to describe a MIMO radar system and to study information theoretic adaptive radar waveform design for detection and estimation of multiple radar targets within this framework. Specifically, this dissertation first provides a new compact vector channel model for representing a MIMO radar system which illustrates the parallel composition of radar systems and information transmission systems. Second, using the proposed framework this dissertation contributes a compressed sensing based information theoretic approach to waveform design for the detection of multiple extended targets in noiseless and noisy scenarios. Third, this dissertation defines the multiple extended target estimation problem within the framework and proposes a greedy signal to interference-plus-noise ratio (SINR) maximizing procedure based on a similar approach developed for a collaborative multibase wireless communication system to optimally design wave forms in this scenario

Multi-Antenna Techniques for Next Generation Cellular Communications

Future cellular communications are expected to offer substantial improvements for the pre- existing mobile services with higher data rates and lower latency as well as pioneer new types of applications that must comply with strict demands from a wider range of user types. All of these tasks require utmost efficiency in the use of spectral resources. Deploying multiple antennas introduces an additional signal dimension to wireless data transmissions, which provides a significant alternative solution against the plateauing capacity issue of the limited available spectrum. Multi-antenna techniques and the associated key enabling technologies possess unquestionable potential to play a key role in the evolution of next generation cellular systems. Spectral efficiency can be improved on downlink by concurrently serving multiple users with high-rate data connections on shared resources. In this thesis optimized multi-user multi-input multi-output (MIMO) transmissions are investigated on downlink from both filter design and resource allocation/assignment points of view. Regarding filter design, a joint baseband processing method is proposed specifically for high signal-to-noise ratio (SNR) conditions, where the necessary signaling overhead can be compensated for. Regarding resource scheduling, greedy- and genetic-based algorithms are proposed that demand lower complexity with large number of resource blocks relative to prior implementations. Channel estimation techniques are investigated for massive MIMO technology. In case of channel reciprocity, this thesis proposes an overhead reduction scheme for the signaling of user channel state information (CSI) feedback during a relative antenna calibration. In addition, a multi-cell coordination method is proposed for subspace-based blind estimators on uplink, which can be implicitly translated to downlink CSI in the presence of ideal reciprocity. Regarding non-reciprocal channels, a novel estimation technique is proposed based on reconstructing full downlink CSI from a select number of dominant propagation paths. The proposed method offers drastic compressions in user feedback reports and requires much simpler downlink training processes. Full-duplex technology can provide up to twice the spectral efficiency of conventional resource divisions. This thesis considers a full-duplex two-hop link with a MIMO relay and investigates mitigation techniques against the inherent loop-interference. Spatial-domain suppression schemes are developed for the optimization of full-duplex MIMO relaying in a coverage extension scenario on downlink. The proposed methods are demonstrated to generate data rates that closely approximate their global bounds

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Contribution à la mise en oeuvre de récepteurs et de techniques d'estimation de canal pour les systèmes mobiles de DS-CDMA multi-porteuse

Ce mémoire traite du développement de récepteurs et de techniques déstimation de canal pour les systèmes mobiles sans fil de type DS-CDMA multi-porteuse. Deux problèmes principaux doivent être pris en compte dans ce cas. Premièrement, l'Interférence d'Accès Multiple (IAM) causée par d'autres utilisateurs. Deuxièmement, les propriétés des canaux de propagation dans les systèmes radio mobiles. Ainsi, dans la première partie du manuscrit, nous proposons deux structures adaptatives (dites détection séparée et détection jointe) pour la mise en oeuvre de récepteurs minimisant lérreur quadratique moyenne (MMSE), fondés sur un Algorithme de Projection Affine (APA). Ces récepteurs permettent de supprimer les IAM, notamment lorsque le canal d'évanouissement est invariant dans le temps. Cependant, comme ces récepteurs nécessitent les séquences d'apprentissage de chaque utilisateur actif, nous développons ensuite deux récepteurs adaptatifs dits aveugles, fondés sur un algorithme de type projection affine. Dans ce cas, seule la séquence d'étalement de l'utilisateur désiré est nécessaire. Quand les séquences d'étalement de tous les utilisateurs sont disponibles, un récepteur reposant sur le décorrélateur est aussi proposé et permet d'éliminer les IAM, sans qu'une période pour l'adaptation soit nécessaire. Dans la seconde partie, comme la mise en oeuvre de récepteurs exige léstimation du canal, nous proposons plusieurs algorithmes pour léstimation des canaux d'évanouissement de Rayleigh, variables dans le temps et produits dans les systèmes multi-porteuses. A cette fin, les canaux sont approximés par des processus autorégressifs (AR) d'ordre supérieur à deux. Le premier algorithme repose sur deux filtres de Kalman interactifs pour léstimation conjointe du canal et de ses paramètres AR. Puis, pour nous affranchir des hypothèses de gaussianité nécessaires à la mise en oeuvre d'un filtre optimal de Kalman, nous étudions la pertinence d'une structure fondée sur deux filtres H1 interactifs. Enfin, léstimation de canal peut ^etre vue telle un problème déstimation fondée sur un modèle à erreur- sur-les-variables (EIV). Les paramètres AR du canal et les variances de processus générateur et du bruit d'observation dans la représentation de léspace d'état du système sont dans ce cas estimés conjointement à partir du noyau des matrices d'autocorrélation appropriées.This dissertation deals with the development of receivers and channel estimation techniques for multi-carrier DS- CDMA mobile wireless systems. Two major problems should be taken into account in that case. Firstly, the Multiple Access Interference (MAI) caused by other users. Secondly, the multi-path fading of mobile wireless channels. In the first part of the dissertation, we propose two adaptive structures (called separate and joint detection) to design Minimum Mean Square Error (MMSE) receivers, based on the Affine Projection Algorithm (APA). These receivers are able to suppress the MAI, particularly when the fading channel is time-invariant. However, as they require a training sequence for every active user, we then propose two blind adaptive multiuser receiver structures based on a blind APA-like multiuser detector. In that case, only the knowledge of the spreading code of the desired user is required. When the spreading codes of all users are available, a decorrelating detector based receiver is proposed and is able to completely eliminate the MAI without any training. In the second part, as receiver design usually requires the estimation of the channel, we propose several training-based algorithms for the estimation of time-varying Rayleigh fading channels in multi-carrier systems. For this purpose, the fading channels are approximated by autoregressive (AR) processes whose order is higher than two. The first algorithm makes it possible to jointly estimate the channel and its AR parameters based on two-cross-coupled Kalman filters. Nevertheless, this filtering is based on restrictive Gaussian assumptions. To relax them, we investigate the relevance of a structure based on two-cross-coupled H1 filters. This method consists in minimizing the influence of the disturbances such as the additive noise on the estimation error. Finally, we propose to view the channel estimation as an Errors-In-Variables (EIV) issue. In that case, the channel AR parameters and the variances of both the driving process and the measurement noise in the state-space representation of the system are estimated from the null space of suitable correlation matrices

Analysis of data-aided channel tracking for hybrid massive MIMO systems in millimeter wave communications

As the data traffic in future wireless communications will explosively grow up to 1000 folds by the deployment of 5G, several technologies are emerging to satisfy this demand, including massive multiple-input multiple-output (MIMO), millimeter wave(mmWave) communications, Non-Orthogonal Multiple Access (NOMA), etc. The combination of millimeter wave communication and massive MIMO is a promising solution since it can provide tens of GHz bandwidth by fundamentally exploring higher unoccupied spectrum resources. As the wavelength of higher frequency shrinks, it is possible to design more compact antenna array with a very large number of antennas. However, this will cause enormous hardware cost, energy consumption and computation complexity of decent RF(Radio Frequency) chains. To this end, spatial sparsity is widely explored to enable hybrid mmWave massive MIMO systems with limited RF chains to achieve high spectral and energy efficiency. On the other hand, channel estimation problem for systems with limited RF chains is quite challenging due to the unaffordable overhead. To be specific, the conventional pilot-based channel estimation requires to repeatedly transmit the same pilot because only a limited number of antennas will be activated for each time slot. Therefore, it consumes a huge amount of temporal and spectral resources. To overcome this problem, channel estimation for mmWave massive MIMO systems is still an on-going research area. Among plenty of candidates, channel tracking is the most promising one. To achieve the extremely low cost and complexity, which is also the greatest motivation of this thesis, data-aided channel tracking method is thoroughly investigated with closed-form CRLB(Cram´er-Rao lower bound). In this thesis, data-aided channel tracking systems with different types of antenna, including ULA(Uniform Linear Antenna array), DLA(Discrete Lens Antenna ar ray) and UPA(Uniform Planar Antenna array), are comprehensively studied and proposed, and the closed-form expressions of the corresponding CRLBs are carefully derived. The numerical results of the simulations for each case are shown respectively, and they reveal that the performance of the proposed data-aided channel tracking system approaches the CRLB very well. In addition, to further explore the data-aided channel tracking system, the multi-user scenario is investigated in this thesis. This is motivated by the highway and high-speed railway application, where overtaking operation happens frequently. In this case, the users in the same beam suffer from high channel interference, thus degrading the channel estimation performance or even causing outage. To deal with this issue, we proposed an estimated SER(Symbol Error Rate) metric to indicate if a scheduling operation is necessary to be taken place and restart of the whole channel tracking system is required. This metric is included as the Update phase in the proposed channel tracking method for multiuser scenario with DLA. The theoretical SER closed-form expression is also derived for multi-user data detection. The numerical results of the simulations verified the theoretical SER expression, and the scheduling metric based on the estimated SER performance is also discussed

l0 Sparse signal processing and model selection with applications

Sparse signal processing has far-reaching applications including compressed sensing, media compression/denoising/deblurring, microarray analysis and medical imaging. The main reason for its popularity is that many signals have a sparse representation given that the basis is suitably selected. However the difficulty lies in developing an efficient method of recovering such a representation. To this aim, two efficient sparse signal recovery algorithms are developed in the first part of this thesis. The first method is based on direct minimization of the l0 norm via cyclic descent, which is called the L0LS-CD (l0 penalized least squares via cyclic descent) algorithm. The other method minimizes smooth approximations of sparsity measures including those of the l0 norm via the majorization minimization (MM) technique, which is called the QC (quadratic concave) algorithm. The L0LS-CD algorithm is developed further by extending it to its multivariate (V-L0LS-CD (vector L0LS-CD)) and group (gL0LS-CD (group L0LS-CD)) regression variants. Computational speed-ups to the basic cyclic descent algorithm are discussed and a greedy version of L0LS-CD is developed. Stability of these algorithms is analyzed and the impact of the penalty parameter and proper initialization on the algorithm performance are highlighted. A suitable method for performance comparison of sparse approximating algorithms in the presence of noise is established. Simulations compare L0LS-CD and V-L0LS-CD with a range of alternatives on under-determined as well as over-determined systems. The QC algorithm is applicable to a class of penalties that are neither convex nor concave but have what we call the quadratic concave property. Convergence proofs of this algorithm are presented and it is compared with the Newton algorithm, concave convex (CC) procedure, as well as with the class of proximity algorithms. Simulations focus on the smooth approximations of the l0 norm and compare them with other l0 denoising algorithms. Next, two applications of sparse modeling are considered. In the first application the L0LS-CD algorithm is extended to recover a sparse transfer function in the presence of coloured noise. The second uses gL0LS-CD to recover the topology of a sparsely connected network of dynamic systems. Both applications use Laguerre basis functions for model expansion. The role of model selection in sparse signal processing is widely neglected in literature. The tuning/penalty parameter of a sparse approximating problem should be selected using a model selection criterion which minimizes a desired discrepancy measure. Compared to the commonly used model selection methods, the SURE (Stein's unbiased risk estimator) estimator stands out as one which does not suffer from the limitations of other methods. Most model selection criterion are developed based on signal or prediction mean squared error. The last section of this thesis develops an SURE criterion instead for parameter mean square error and applies this result to l1 penalized least squares problem with grouped variables. Simulations based on topology identification of a sparse network are presented to illustrate and compare with alternative model selection criteria

Applications of compressive sensing to direction of arrival estimation

Die Schätzung der Einfallsrichtungen (Directions of Arrival/DOA) mehrerer ebener Wellenfronten mit Hilfe eines Antennen-Arrays ist eine der prominentesten Fragestellungen im Gebiet der Array-Signalverarbeitung. Das nach wie vor starke Forschungsinteresse in dieser Richtung konzentriert sich vor allem auf die Reduktion des Hardware-Aufwands, im Sinne der Komplexität und des Energieverbrauchs der Empfänger, bei einem vorgegebenen Grad an Genauigkeit und Robustheit gegen Mehrwegeausbreitung. Diese Dissertation beschäftigt sich mit der Anwendung von Compressive Sensing (CS) auf das Gebiet der DOA-Schätzung mit dem Ziel, hiermit die Komplexität der Empfängerhardware zu reduzieren und gleichzeitig eine hohe Richtungsauflösung und Robustheit zu erreichen. CS wurde bereits auf das DOA-Problem angewandt unter der Ausnutzung der Tatsache, dass eine Superposition ebener Wellenfronten mit einer winkelabhängigen Leistungsdichte korrespondiert, die über den Winkel betrachtet sparse ist. Basierend auf der Idee wurden CS-basierte Algorithmen zur DOA-Schätzung vorgeschlagen, die sich durch eine geringe Rechenkomplexität, Robustheit gegenüber Quellenkorrelation und Flexibilität bezüglich der Wahl der Array-Geometrie auszeichnen. Die Anwendung von CS führt darüber hinaus zu einer erheblichen Reduktion der Hardware-Komplexität, da weniger Empfangskanäle benötigt werden und eine geringere Datenmenge zu verarbeiten und zu speichern ist, ohne dabei wesentliche Informationen zu verlieren. Im ersten Teil der Arbeit wird das Problem des Modellfehlers bei der CS-basierten DOA-Schätzung mit gitterbehafteten Verfahren untersucht. Ein häufig verwendeter Ansatz um das CS-Framework auf das DOA-Problem anzuwenden ist es, den kontinuierlichen Winkel-Parameter zu diskreditieren und damit ein Dictionary endlicher Größe zu bilden. Da die tatsächlichen Winkel fast sicher nicht auf diesem Gitter liegen werden, entsteht dabei ein unvermeidlicher Modellfehler, der sich auf die Schätzalgorithmen auswirkt. In der Arbeit wird ein analytischer Ansatz gewählt, um den Effekt der Gitterfehler auf die rekonstruierten Spektra zu untersuchen. Es wird gezeigt, dass sich die Messung einer Quelle aus beliebiger Richtung sehr gut durch die erwarteten Antworten ihrer beiden Nachbarn auf dem Gitter annähern lässt. Darauf basierend wird ein einfaches und effizientes Verfahren vorgeschlagen, den Gitterversatz zu schätzen. Dieser Ansatz ist anwendbar auf einzelne Quellen oder mehrere, räumlich gut separierte Quellen. Für den Fall mehrerer dicht benachbarter Quellen wird ein numerischer Ansatz zur gemeinsamen Schätzung des Gitterversatzes diskutiert. Im zweiten Teil der Arbeit untersuchen wir das Design kompressiver Antennenarrays für die DOA-Schätzung. Die Kompression im Sinne von Linearkombinationen der Antennensignale, erlaubt es, Arrays mit großer Apertur zu entwerfen, die nur wenige Empfangskanäle benötigen und sich konfigurieren lassen. In der Arbeit wird eine einfache Empfangsarchitektur vorgeschlagen und ein allgemeines Systemmodell diskutiert, welches verschiedene Optionen der tatsächlichen Hardware-Realisierung dieser Linearkombinationen zulässt. Im Anschluss wird das Design der Gewichte des analogen Kombinations-Netzwerks untersucht. Numerische Simulationen zeigen die Überlegenheit der vorgeschlagenen kompressiven Antennen-Arrays im Vergleich mit dünn besetzten Arrays der gleichen Komplexität sowie kompressiver Arrays mit zufällig gewählten Gewichten. Schließlich werden zwei weitere Anwendungen der vorgeschlagenen Ansätze diskutiert: CS-basierte Verzögerungsschätzung und kompressives Channel Sounding. Es wird demonstriert, dass die in beiden Gebieten durch die Anwendung der vorgeschlagenen Ansätze erhebliche Verbesserungen erzielt werden können.Direction of Arrival (DOA) estimation of plane waves impinging on an array of sensors is one of the most important tasks in array signal processing, which have attracted tremendous research interest over the past several decades. The estimated DOAs are used in various applications like localization of transmitting sources, massive MIMO and 5G Networks, tracking and surveillance in radar, and many others. The major objective in DOA estimation is to develop approaches that allow to reduce the hardware complexity in terms of receiver costs and power consumption, while providing a desired level of estimation accuracy and robustness in the presence of multiple sources and/or multiple paths. Compressive sensing (CS) is a novel sampling methodology merging signal acquisition and compression. It allows for sampling a signal with a rate below the conventional Nyquist bound. In essence, it has been shown that signals can be acquired at sub-Nyquist sampling rates without loss of information provided they possess a sufficiently sparse representation in some domain and that the measurement strategy is suitably chosen. CS has been recently applied to DOA estimation, leveraging the fact that a superposition of planar wavefronts corresponds to a sparse angular power spectrum. This dissertation investigates the application of compressive sensing to the DOA estimation problem with the goal to reduce the hardware complexity and/or achieve a high resolution and a high level of robustness. Many CS-based DOA estimation algorithms have been proposed in recent years showing tremendous advantages with respect to the complexity of the numerical solution while being insensitive to source correlation and allowing arbitrary array geometries. Moreover, CS has also been suggested to be applied in the spatial domain with the main goal to reduce the complexity of the measurement process by using fewer RF chains and storing less measured data without the loss of any significant information. In the first part of the work we investigate the model mismatch problem for CS based DOA estimation algorithms off the grid. To apply the CS framework a very common approach is to construct a finite dictionary by sampling the angular domain with a predefined sampling grid. Therefore, the target locations are almost surely not located exactly on a subset of these grid points. This leads to a model mismatch which deteriorates the performance of the estimators. We take an analytical approach to investigate the effect of such grid offsets on the recovered spectra showing that each off-grid source can be well approximated by the two neighboring points on the grid. We propose a simple and efficient scheme to estimate the grid offset for a single source or multiple well-separated sources. We also discuss a numerical procedure for the joint estimation of the grid offsets of closer sources. In the second part of the thesis we study the design of compressive antenna arrays for DOA estimation that aim to provide a larger aperture with a reduced hardware complexity and allowing reconfigurability, by a linear combination of the antenna outputs to a lower number of receiver channels. We present a basic receiver architecture of such a compressive array and introduce a generic system model that includes different options for the hardware implementation. We then discuss the design of the analog combining network that performs the receiver channel reduction. Our numerical simulations demonstrate the superiority of the proposed optimized compressive arrays compared to the sparse arrays of the same complexity and to compressive arrays with randomly chosen combining kernels. Finally, we consider two other applications of the sparse recovery and compressive arrays. The first application is CS based time delay estimation and the other one is compressive channel sounding. We show that the proposed approaches for sparse recovery off the grid and compressive arrays show significant improvements in the considered applications compared to conventional methods