Optimized decision-feedback equalization for convolutional coding with reduced delay

Error propagation is a significant problem with the decision-feedback equalizer (DFE) at low-to-moderate signal-to-noise ratios. In particular, when a DFE is concatenated with a convolutional code, the burst errors associated with error propagation can severely degrade performance, since the convolutional code is optimized for the additive white Gaussian noise channel. In this paper, we explore the compensation of error propagation in the DFE so as to break up error bursts and improve performance with convolutional codes, without incurring larger overall decoding delay. We propose certain stationary error models and derive a modified DFE (MDFE) based on these models which can compensate for the error propagation. The MDFE differs from the conventional DFE only in its tap values. The incorporation of the bias into the model and the removal of the bias during the design process is discussed. Simulations explore the performance of the MDFE for both uncoded and convolutionally coded systems. With coding, the MDFE can significantly improve on the conventional DFE in terms of bit-error rate, and the MDFE without interleaving can improve on the conventional DFE with interleaving in terms of decision delay.