A Generalized Data Detection Scheme Using Hyperplane for Magnetic Recording Channels With Pattern-Dependent Noise

We propose a novel data-detection scheme using support vector machine techniques in the presence of pattern-dependent noise on magnetic recording channels. First, the log-likelihood ratios (LLRs) of data series were generated using the Bahl-Cocke-Jelinek-Raviv algorithm. Second, these LLRs were mapped to a 3-D space, and hyperplanes for data discrimination were generated using the radial-basis-function kernel. Third, the LLR of each bit was rescaled on the basis of the distance from the hyperplanes and then fed to an LDPC decoder. We evaluated the performance of the proposed method by retrieving a real data series from a perpendicular magnetic recording channel, and obtained a bit-error rate of approximately 10^-3. For projective geometry-low-density parity-check codes with a code rate of 0.93, the proposed method can reduce the iteration number for a sum product algorithm using conventional LLRs by approximately half.