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Author: search.filters.author.Myint, Lin M.M.Subject: search.filters.subject.Viterbi detector

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    Adaptive Designing Process of 2-D GPR Target and Equalizer based on BER in BPMR Systems
    (2020-06-01)
    Rueangnetr, Natthakan
    ;
    Warisarn, Chanon
    ;
    Myint, Lin M.M.
    In high areal density magnetic recording systems, two-dimensional (2-D) equalizer and 2-D generalized partial response (GPR) target are proposed to tackle the 2-D interference problem. However, the minimum mean squared error (MMSE) technique cannot provide the optimality of the 2D detector, particularly only partial side-track information are available in the designing process. Therefore, we develop an adaptive designing process to obtain the optimal equalizers and targets based on bit error rate (BER) for two-track two-head (2T2H) bit-pattern media recording (BPMR) read channel model. In the process, the coefficients of the 2-D equalizer and GPR target are computed using the MMSE technique first. Then these MMSE targets and equalizers are adaptively adjusted based on BER at the low SNR levels. In each iteration, the main side-track coefficients of the 2×3 GPR targets are adjusted based on the BER. The iterative process is continually operated until there will be enough low level of error. Finally, the performance of those equalizers and targets are evaluated under the 2T2H BPMR system.
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    A comparative study on reduced complexity 2-D detectors for bit patterned media
    (2011-12-01)
    Myint, Lin M.M.
    ;
    Supnithi, Pornchai
    In this paper, we compare the performance of the reduced complexity 2-D detection methods: Graph-based detection and Viterbi-based detection for the 2-D interference bit patterned media channels at various areal densities. In the graph-based detection, the complexity is reduced by ignoring the ITI from the cornered islands in designing the factor graph. In Viterbi-based detector, a trellis for the ISI only is considered. However, both methods improve the ITI mitigation with the help of multi-track processing and iterative processing. Between them, the graph-based detection needs higher computational complexity than the Viterbi-based detection. However, the graph-based detection method provides a significant performance gain over the Viterbi-based detect.on © 2011 IEEE.