Interference suppression and parameter estimation in wireless communication systems over time-varing multipath fading channels

This dissertation focuses on providing solutions to two of the most important problems in wireless communication systems design, namely, 1) the interference suppression, and 2) the channel parameter estimation in wireless communication systems over time-varying multipath fading channels. We first study the interference suppression problem in various communication systems under a unified multirate transmultiplexer model. A state-space approach that achieves the optimal realizable equalization (suppression of inter-symbol interference) is proposed, where the Kalman filter is applied to obtain the minimum mean squared error estimate of the transmitted symbols. The properties of the optimal realizable equalizer are analyzed. Its relations with the conventional equalization methods are studied. We show that, although in general a Kalman filter has an infinite impulse response, the Kalman filter based decision-feedback equalizer (Kalman DFE) is a finite length filter. We also propose a novel successive interference cancellation (SIC) scheme to suppress the inter-channel interference encountered in multi-input multi-output systems. Based on spatial filtering theory, the SIC scheme is again converted to a Kalman filtering problem. Combining the Kalman DFE and the SIC scheme in series, the resultant two-stage receiver achieves optimal realizable interference suppression. Our results are the most general ever obtained, and can be applied to any linear channels that have a state-space realization, including time-invariant, time-varying, finite impulse response, and infinite impulse response channels. The second half of the dissertation devotes to the parameter estimation and tracking of single-input single-output time-varying multipath channels. We propose a novel method that can blindly estimate the channel second order statistics (SOS). We establish the channel SOS identifiability condition and propose novel precoder structures that guarantee the blind estimation of the channel SOS and achieve diversities. The estimated channel SOS can then be fit into a low order autoregressive (AR) model characterizing the time evolution of the channel impulse response. Based on this AR model, a new approach to time-varying multipath channel tracking is proposed

Blind adaptive constrained reduced-rank parameter estimation based on constant modulus design for CDMA interference suppression

This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems

Adaptive interference suppression for DS-CDMA systems based on interpolated FIR filters with adaptive interpolators in multipath channels

In this work we propose an adaptive linear receiver structure based on interpolated finite impulse response (FIR) filters with adaptive interpolators for direct sequence code division multiple access (DS-CDMA) systems in multipath channels. The interpolated minimum mean-squared error (MMSE) and the interpolated constrained minimum variance (CMV) solutions are described for a novel scheme where the interpolator is rendered time-varying in order to mitigate multiple access interference (MAI) and multiple-path propagation effects. Based upon the interpolated MMSE and CMV solutions we present computationally efficient stochastic gradient (SG) and exponentially weighted recursive least squares type (RLS) algorithms for both receiver and interpolator filters in the supervised and blind modes of operation. A convergence analysis of the algorithms and a discussion of the convergence properties of the method are carried out for both modes of operation. Simulation experiments for a downlink scenario show that the proposed structures achieve a superior BER convergence and steady-state performance to previously reported reduced-rank receivers at lower complexity

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