Turbo equalization via constrained-delay APP estimation with decision feedback

We consider turbo equalization for intersymbol interference (ISI) channels, wherein soft symbol decisions generated by the channel detector are iteratively exchanged with the outer error-correction decoder based on the turbo principle. Our work is based on low-complexity suboptimal soft-output channel detection using a constrained-delay (CD) a posteriori probability (APP) algorithm. Central to the proposed idea is the incorporation of effective decision-feedback schemes, which significantly reduce complexity while providing immunity against error propagation that typically plagues decision-feedback schemes. We observe that the effect of decision feedback is quite different on turbo equalization versus traditional, hard-decision-generating and noniterative equalization. In particular, we demonstrate that when the feedback scheme applied is inadequate for the given equalizer parameters and ISI condition, the extrinsic information generated by the equalizer becomes distinctly non-Gaussian, and the quality of soft information, as monitored by the trajectory of mutual information, fails to improve in the iterative process. We identify parameters of feedback-based CD-APP schemes that offer favorable complexity/performance tradeoffs, compared with existing turbo-equalization techniques.