Detection for signal-dependent correlated noise in magnetic recording

Abstract

At high linear recording densities, signal-dependent, correlated noise, due to the medium and correlated noise resulting from equalization, significantly degrades the error-rate performance of the Viterbi algorithm (VA). Longer length partial-response targets can alleviate this problem, however, only by increasing the state complexity of the VA. Two options for dealing with this problem are examined in this paper. The first option is class IV equalization followed by a VA that employs a modified branch metric specifically designed for correlated noise. This results in a substantially higher complexity VA. The other option involves adaptively locating an equalization target that limits noise correlation prior to Viterbi detection by placing a monic constraint on the target. Using data collected from state-of-the-art magnetic recording components, it is shown that monic target equalization followed by the normal VA significantly outperforms class IV equalization followed by the modified branch metric VA of similar complexity as well as conventional PRML.