کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1802396 | 1024596 | 2008 | 4 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Performance of a Chase-type decoding algorithm for Reed-Solomon codes on perpendicular magnetic recording channels
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
فیزیک ماده چگال
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Performance of a Chase-type decoding algorithm for Reed-Solomon codes on perpendicular magnetic recording channels Performance of a Chase-type decoding algorithm for Reed-Solomon codes on perpendicular magnetic recording channels](/preview/png/1802396.png)
چکیده انگلیسی
Algebraic soft-decision Reed-Solomon (RS) decoding algorithms with improved error-correcting capability and comparable complexity to standard algebraic hard-decision algorithms could be very attractive for possible implementation in the next generation of read channels. In this work, we investigate the performance of a low-complexity Chase (LCC)-type soft-decision RS decoding algorithm, recently proposed by Bellorado and KavÄiÄ, on perpendicular magnetic recording channels for sector-long RS codes of practical interest. Previous results for additive white Gaussian noise channels have shown that for a moderately long high-rate code, the LCC algorithm can achieve a coding gain comparable to the Koetter-Vardy algorithm with much lower complexity. We present a set of numerical results that show that this algorithm provides small coding gains, on the order of a fraction of a dB, with similar complexity to the hard-decision algorithms currently used, and that larger coding gains can be obtained if we use more test patterns, which significantly increases its computational complexity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Magnetism and Magnetic Materials - Volume 320, Issue 22, November 2008, Pages 3124-3127
Journal: Journal of Magnetism and Magnetic Materials - Volume 320, Issue 22, November 2008, Pages 3124-3127
نویسندگان
H. Wang, W. Chang, J.R. Cruz,