Adaptive low complexity MAP decoding for turbo equalization

Turbo equalization is a powerful method to iteratively detect and decode convolutionally encoded data that is corrupted by inter symbol interference (ISI) and Gaussian noise. It is based on the exchange of reliability information between the equalizer and the decoder, which is typically some sort of maximum a posteriori (MAP) decoder. While the number of remaining errors in the received sequence decreases during the iteration process, the computational effort for decoding remains unchanged in each iteration. In this paper a syndrome based MAP decoder is proposed, that is capable of reducing the computational decoding effort during the iteration process without significantly influencing the convergence behavior.