Constant Modulus Algorithms via Low-Rank Approximation

We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, as well as the modified constant modulus and the constrained modified constant modulus. The usefulness of the proposed solutions is demonstrated for the tasks of blind beamforming and blind multiuser detection. The performance of these solutions, as we demonstrate by simulated data, is superior to existing methods.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

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

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems

Sparse Nonlinear MIMO Filtering and Identification

In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel-estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three dentification approaches are discussed: conventional identification based on both input and output samples, semi–blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulation

Adaptive Transmitter Precoding for Time Division Duplex CDMA in Fading Multipath Channels: Strategy and Analysis

The recently developed blind adaptive techniques for multiuser detection in code division multiple access (CDMA) systems offer an attractive compromise of performance and complexity. However, the desire to further reduce complexity at the mobile unit has led to the investigation of techniques that move signal processing from the mobile unit to the base station. In this paper, we investigate transmitter precoding for downlink time division duplex (TDD) code division multiple access (CDMA) communications. In particular, we develop a linear minimum mean square error precoding strategy using blind channel estimation for fading multipath channels that allows for simple matched filtering at the mobile unit and is easy to make adaptive. We also present a performance analysis using tools developed for the analysis of conventional (receiver-based) linear blind multiuser detection in unknown channels. We compare the analytical and simulation results to traditional receiver-based blind multiuser detection. It is seen that transmitter precoding offers a reasonable alternative for TDD-mode CDMA when minimizing computational complexity at the mobile unit is a priority

Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers

Monte Carlo algorithms can be used to estimate the state of a system given relative observations. In this dissertation, these algorithms are applied to physical layer communications system models to estimate channel state information, to obtain soft information about transmitted symbols or multiple access interference, or to obtain estimates of all of these by joint estimation. Initially, we develop and analyze a multiple access technique utilizing mutually orthogonal complementary sets (MOCS) of sequences. These codes deliberately introduce inter-chip interference, which is naturally eliminated during processing at the receiver. However, channel impairments can destroy their orthogonality properties and additional processing becomes necessary. We utilize Monte Carlo algorithms to perform joint channel and symbol estimation for systems utilizing MOCS sequences as spreading codes. We apply Rao-Blackwellization to reduce the required number of particles. However, dense signaling constellations, multiuser environments, and the interchannel interference introduced by the spreading codes all increase the dimensionality of the symbol state space significantly. A full maximum likelihood solution is computationally expensive and generally not practical. However, obtaining the optimum solution is critical, and looking at only a part of the symbol space is generally not a good solution. We have sought algorithms that would guarantee that the correct transmitted symbol is considered, while only sampling a portion of the full symbol space. The performance of the proposed method is comparable to the Maximum Likelihood (ML) algorithm. While the computational complexity of ML increases exponentially with the dimensionality of the problem, the complexity of our approach increases only quadratically. Markovian structures such as the one imposed by MOCS spreading sequences can be seen in other physical layer structures as well. We have applied this partitioning approach with some modification to blind equalization of frequency selective fading channel and to multiple-input multiple output receivers that track channel changes. Additionally, we develop a method that obtains a metric for quantifying the convergence rate of Monte Carlo algorithms. Our approach yields an eigenvalue based method that is useful in identifying sources of slow convergence and estimation inaccuracy.Ph.D.Committee Chair: Douglas B. Williams; Committee Member: Brani Vidakovic; Committee Member: G. Tong zhou; Committee Member: Gordon Stuber; Committee Member: James H. McClella