Soft-output post-processing detection for PRML channels in the presence of data-dependent media noise

A soft-output data-detection scheme optimized for data-dependent media-noise recording channels is proposed that uses signal-dependent correlation-sensitive (SDCS) metric estimation for post-processing decoding. This media-noise soft-output (MNS) decoding scheme achieves sub-optimal maximum-likelihood (ML) sequence detection in a non-stationary media-noise channel, while still using traditional Viterbi detection. Because it drastically reduces SDCS metric computation by focusing on only specified dominant error-events in the ML detector, it is less complex than other sub-optimal detection schemes. Moreover, its one-shot post-processing scheme enables the use of a simple lookup-table architecture suitable for high-speed circuit implementation. Simulation shows that the MNS decoding scheme in conjunction with a conventional ME²PRML system provides an excellent tradeoff between data-detection performance and computation complexity for a media-noise-dominant high-density recording channel.