Performance analysis of decision-feedback equalization with maximum-likelihood detector in high-density recording channels

The performance of decision-feedback equalization combined with maximum-likelihood detection (DFE/ML) using the FDTS/DF algorithm was estimated analytically in terms of the length of the feedback-filter and the depth of the ML-detector. Performance degradation due to error propagation in the feed-back loop and in the ML-detector was taken into account. It was quantitatively shown that signal-to-noise-ratio performance in high-density magnetic recording channels can be improved by combining an ML-detector and a feedback-filter. In particular, it was found that near-optimum performance can be achieved over the channel density range from 2 to 3 by increasing the total of the feedback-filter length and the ML-detector depth to six bits.