A Systematic Framework for Iterative Maximum Likelihood Receiver Design

Abstract

In this paper, we link the turbo principle to unconstrained maximum likelihood (ML) sequence detection and joint ML parameter estimation. First, we demonstrate for memoryless channels with complete channel state information how the turbo decoder can be systematically derived starting from the ML sequence detection criterion. In particular, we show that a method to solve the ML sequence detection problem is to iteratively solve the corresponding critical point equations of an equivalent unconstrained estimation problem by means of fixed-point iterations. The turbo decoding algorithm is obtained by approximating the overall a posteriori probabilities. Subsequently, we show how this general approximative iterative maximum likelihood (AIML) framework can be applied to general iterative ML receiver design. We consider static memoryless channels with unknown channel parameters. The time-selective fading channels with partial channel state information is the subject of a companion paper.