Performance analysis of soft-output post-processing detection for data-dependent media noise channels

A soft-output data-detection scheme optimized for data-dependent media-noise recording channels is investigated that uses signal-dependent correlation-sensitive (SDCS) metric computation for post-processing decoding. This media-noise soft-output (MNS) decoding scheme achieves suboptimal maximum-likelihood (ML) sequence detection in a nonstationary media-noise channel, while still using traditional Viterbi detection. Because it drastically reduces SDCS metric computation by focusing on correcting only the dominant error events in the ML detector, it is less complex than other suboptimal detection schemes. Moreover, the MNS decoding scheme provides parity-check-code decoding with more reliable soft-output information. Simulation showed that MNS decoding in conjunction with a conventional ME²PRML system, provides an excellent tradeoff between data-detection performance and computation complexity for a jitter-noise dominant high-density recording channel.