An Iteratively Decodable Tensor Product Code with Application to Data Storage

The error pattern correcting code (EPCC) can be constructed to provide a syndrome decoding table targeting the dominant error events of an inter-symbol interference channel at the output of the Viterbi detector. For the size of the syndrome table to be manageable and the list of possible error event...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 28; no. 2; pp. 228 - 240
Main Authors: Alhussien, Hakim, Moon, Jaekyun
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Complexity
Decoding
detection postprocessing
Detectors
Error correction
Error correction codes
Error detection
error-pattern correction
Event detection
inter-symbol interference
Interference channels
Iterative decoding
Mathematical analysis
Memory
multilevel log-likelihood ratio
Permissible error
Product codes
q-ary LDPC
Redundancy
signature correcting code
Tables (data)
Tensile stress
Tensor product codes
tensor symbol signatures
Tensors
turbo equalization
Viterbi algorithm
ISSN:0733-8716, 1558-0008
Online Access:Get full text
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Summary:The error pattern correcting code (EPCC) can be constructed to provide a syndrome decoding table targeting the dominant error events of an inter-symbol interference channel at the output of the Viterbi detector. For the size of the syndrome table to be manageable and the list of possible error events to be reasonable in size, the codeword length of EPCC needs to be short enough. However, the rate of such a short length code will be too low for hard drive applications. To accommodate the required large redundancy, it is possible to record only a highly compressed function of the parity bits of EPCC's tensor product with a symbol correcting code. In this paper, we show that the proposed tensor error-pattern correcting code (T-EPCC) is linear time encodable and also devise a low-complexity soft iterative decoding algorithm for EPCC's tensor product with q-ary LDPC (T-EPCC-qLDPC). Simulation results show that T-EPCC-qLDPC achieves almost similar performance to single-level qLDPC with a 1/2 KB sector at 50% reduction in decoding complexity. Moreover, 1 KB T-EPCC-qLDPC surpasses the performance of 1/2 KB single-level qLDPC at the same decoder complexity.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2010.100212