Abstract

In this thesis, iterative detection and estimation algorithms for digital communications systems in the presence of parametric uncertainty are explored and further developed. In particular, variational methods, which have been extensively applied in other research fields such as artificial intelligence and machine learning, are introduced and systematically used in deriving approximations to the optimal receivers in various channel conditions. The key idea behind the variational methods is to transform the problem of interest into an optimization problem via an introduction of extra degrees of freedom known as variational parameters. This is done so that, for fixed values of the free parameters, the transformed problem has a simple solution, solving approximately the original problem.

The thesis contributes to the state of the art of advanced receiver design in a number of ways. These include the development of new theoretical and conceptual viewpoints of iterative turbo-processing receivers as well as a new set of practical joint estimation and detection algorithms. Central to the theoretical studies is to show that many of the known low-complexity turbo receivers, such as linear minimum mean square error (MMSE) soft-input soft-output (SISO) equalizers and demodulators that are based on the Bayesian expectation-maximization (BEM) algorithm, can be formulated as solutions to the variational optimization problem. This new approach not only provides new insights into the current designs and structural properties of the relevant receivers, but also suggests some improvements on them.

In addition, SISO detection in multipath fading channels is considered with the aim of obtaining a new class of low-complexity adaptive SISOs. As a result, a novel, unified method is proposed and applied in order to derive recursive versions of the classical Baum-Welch algorithm and its Bayesian counterpart, referred to as the BEM algorithm. These formulations are shown to yield computationally attractive soft decision-directed (SDD) channel estimators for both deterministic and Rayleigh fading intersymbol interference (ISI) channels.

Next, by modeling the multipath fading channel as a complex bandpass autoregressive (AR) process, it is shown that the statistical parameters of radio channels, such as frequency offset, Doppler spread, and power-delay profile, can be conveniently extracted from the estimated AR parameters which, in turn, may be conveniently derived via an EM algorithm. Such a joint estimator for all relevant radio channel parameters has a number of virtues, particularly its capability to perform equally well in a variety of channel conditions.

Lastly, adaptive iterative detection in the presence of phase uncertainty is investigated. As a result, novel iterative joint Bayesian estimation and symbol a posteriori probability (APP) computation algorithms, based on the variational Bayesian method, are proposed for both constant-phase channel models and dynamic phase models, and their performance is evaluated via computer simulations.