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Author Sawaguchi, H. ♦ Nishida, Y. ♦ Nakagawa, T.
Sponsorship IEEE Magnetics Society
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1965
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Physics ♦ Electricity & electronics ♦ Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Performance analysis ♦ Maximum likelihood decoding ♦ Detectors ♦ Event detection ♦ Magnetic recording ♦ Error correction ♦ Magnetic noise ♦ Noise reduction ♦ Maximum likelihood detection ♦ Statistics
Abstract 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 ME/sup 2/PRML system, provides an excellent tradeoff between data-detection performance and computation complexity for a jitter-noise dominant high-density recording channel.
Description Author affiliation :: San Jose Res. Center, Hitachi Global Storage Technol. Inc., San Jose, CA, USA
ISSN 00189464
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2004-07-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 40
Issue Number 4
Size (in Bytes) 198.63 kB
Page Count 3
Starting Page 3108
Ending Page 3110


Source: IEEE Xplore Digital Library