Generalized versions of turbo decoding in the framework of Bayesian networks and Pearl´s belief propagation algorithm

We use the framework of Bayesian networks to introduce generalizations of the traditional turbo decoding algorithm. We show that traditional turbo decoding represents one of many ways in which the framework of Pearl´s belief propagation algorithm (1988) can be applied for decoding of turbo codes. Simulation results show that a noisy received block which does not converge using traditional turbo decoding can converge to the correct value with one or more of the generalizations introduced here. Though we consider only the case of turbo codes with two constituent decoders here, these methods can be extended in a straightforward manner to codes with larger numbers of constituent decoders