Thesis
Iterative detection and decoding for the four-rectangular-grain TDMR model
Washington State University
Master of Science (MS), Washington State University
2013
Handle:
https://hdl.handle.net/2376/103143
Abstract
This thesis considers detection and error control coding for the two-dimensional magnetic recording (TDMR) channel modeled by the two-dimensional (2D) fourrectangular-grain model proposed by Kavcic, Huang et al. in 2010. This simple model captures the effects of different 2D grain sizes and shapes, as well as the TDMR grain overwrite effect: grains large enough to be written by successive bits retain the polarity of only the last bit written. We present a trellis for the channel possessing reasonable complexity while eliminating geometrically invalid states. A row-by-row BCJR detection algorithm is constructed based on the trellis that considers outputs from two rows at a time over two adjacent columns, thereby enabling consideration of more grain and data states than previously proposed algorithms that scan only two outputs at time. The proposed algorithm employs soft-decision feedback of grain states from previous rows to aid the estimation of current data bits and grain states. Simulation results using the same average coded bit density and serially concatenated convolutional code (SCCC) as a previous paper by Pan, Ryan, et al. show gains in user bits/grain of up to 6.7% over the previous work when no iteration is performed between the TDMR BCJR and iv the SCCC, and gains of up to 13.4% when the detector and the decoder iteratively exchange soft information.
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Details
- Title
- Iterative detection and decoding for the four-rectangular-grain TDMR model
- Creators
- Michael Joseph Carosino
- Contributors
- Benjamin Belzer (Degree Supervisor)Krishnamoorthy Sivakumar (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525381601842
- Language
- English
- Resource Type
- Thesis