Cyclic and quasi-cyclic LDPC codes: New developments

Huang, Qin; Diao, Qiuju; Lin, Shu; Abdel-Ghaffar, Khaled

doi:10.1109/ita.2011.5743581

Cited by 9 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a consequence, they have very low error-floors. FG-LDPC codes that can achieve a BER below 10 −15 have been reported recently [19], [20]. The techniques developed can be modified to analyze the trapping set structure of several classes of QC-LDPC codes constructed based on finite field and experimental designs, such as Latin squares.…”

Section: Conclusion and Remarksmentioning

confidence: 99%

Trapping set structure of LDPC codes on finite geometries

Diao¹,

Tai²,

Lin³

et al. 2013

2013 Information Theory and Applications Workshop (ITA)

View full text Add to dashboard Cite

Abstract-The trapping set structure of LDPC codes constructed based on finite geometries, called finite geometry (FG) LDPC codes, is analyzed using a geometric approach. In this approach, trapping sets in the Tanner graph of an FG-LDPC code are represented by subgeometries of the geometry based on which the code is constructed. Using this geometrical representation, bounds and configurations of trapping sets of an FG-LDPC code can be derived and analyzed.

show abstract

Section: Conclusion and Remarksmentioning

confidence: 99%

Trapping set structure of LDPC codes on finite geometries

Diao¹,

Tai²,

Lin³

et al. 2013

2013 Information Theory and Applications Workshop (ITA)

View full text Add to dashboard Cite

show abstract

“…Storage systems typically require frame-error-rates lower than 10 −15 , making the design of LDPC codes with low errorfloors necessary for applications to flash memory. This topic has generated a significant amount of recent research including [15] [16] [17] [18] [19].…”

Section: B Basics Of Ldpc Codesmentioning

confidence: 99%

Mutual-Information Optimized Quantization for LDPC Decoding of Accurately Modeled Flash Data

Wang,

Dong,

Zhang

et al. 2012

Preprint

View full text Add to dashboard Cite

High-capacity NAND flash memories use multi-level cells (MLCs) to store multiple bits per cell and achieve high storage densities. Higher densities cause increased raw bit error rates (BERs), which demand powerful error correcting codes. Low-density parity-check (LDPC) codes are a well-known class of capacity-approaching codes in AWGN channels. However, LDPC codes traditionally use soft information while the flash read channel provides only hard information. Low resolution soft information may be obtained by performing multiple reads per cell with distinct word-line voltages.We select the values of these word-line voltages to maximize the mutual information between the input and output of the equivalent multiple-read channel under any specified noise model. Our results show that maximum mutual-information (MMI) quantization provides better soft information for LDPC decoding given the quantization level than the constant-pdf-ratio quantization approach. We also show that adjusting the LDPC code degree distribution for the quantized setting provides a significant performance improvement.

show abstract

“…Designing LDPC codes with low error-floors is crucial for the flash memory application since storage systems usually require block-error-rates lower than 10 −15 . This topic has generated a significant amount of recent research including [15] [16] [17] [18] [19] [20].…”

Section: B Basics Of Ldpc Codesmentioning

confidence: 99%

LDPC Decoding with Limited-Precision Soft Information in Flash Memories

Wang¹,

Dong²,

Courtade³

et al. 2012

Preprint

View full text Add to dashboard Cite

This paper investigates the application of low-density parity-check (LDPC) codes to Flash memories.Multiple cell reads with distinct word-line voltages provide limited-precision soft information for the LDPC decoder. The values of the word-line voltages (also called reference voltages) are optimized by maximizing the mutual information (MI) between the input and output of the multiple-read channel.Constraining the maximum mutual-information (MMI) quantization to enforce a constant-ratio constraint provides a significant simplification with no noticeable loss in performance.Our simulation results suggest that for a well-designed LDPC code, the quantization that maximizes the mutual information will also minimize the frame error rate. However, care must be taken to design the code to perform well in the quantized channel. An LDPC code designed for a full-precision Gaussian channel may perform poorly in the quantized setting. Our LDPC code designs provide an example where

show abstract

Cyclic and quasi-cyclic LDPC codes: New developments

Cited by 9 publications

References 41 publications

Trapping set structure of LDPC codes on finite geometries

Trapping set structure of LDPC codes on finite geometries

Mutual-Information Optimized Quantization for LDPC Decoding of Accurately Modeled Flash Data

LDPC Decoding with Limited-Precision Soft Information in Flash Memories

Contact Info

Product

Resources

About