Rate-Adaptive Protograph LDPC Codes for Multi-Level-Cell NAND Flash Memory

Chen, Pingping; Cai, Kui; Zheng, Shuang

doi:10.1109/lcomm.2018.2814985

Cited by 37 publications

(29 citation statements)

References 11 publications

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“…, s dv−1 ) ∈ S dv−1 is a realization of S, dim(s) = d v − 1 is the dimension of s, and x ∈ X is a realization of X. Given P L,S|X , the design of Q v is equivalent to the design of Q * in (1) by setting Y = L × S dv−1 and Z = R. We can solve this design problem by using the DP method proposed in [20], after listing (l, s) in descending order based on P L,S|X (l, s|0)/P L,S|X (l, s|1) (see (2)). After designing Q v , a LUT is typically used for storing Q v , and the output message R is passed to the VN's neighbour CNs, with P R|X given by…”

Section: B Mim-lut Decoder Design For Regular Ldpc Codesmentioning

confidence: 99%

“…Low-density parity-check (LDPC) codes [1] have been widely applied to communication and data storage systems due to their capacity approaching performance. Many of these systems, such as the NAND flash memory, have strict requirements on the memory consumption and implementation complexity of LDPC decoding [2]- [4]. For the sake of simple hardware implementation, many efforts have been devoted to efficiently represent messages for LDPC decoding [4]- [18].…”

Section: Introductionmentioning

confidence: 99%

“…(x) = 0.052632x 6 + 0.902256x 7 + 0.045113x 8 , ρ(2) (x) = 0.32338x 7 + 0.67662x 8 , ρ (3) (x) = 0.372093x 7 + 0.627907x 8 ,and VN degree distributions are given byθ (1) (x) = 0.240602x + 0.210526x 2 + 0.030075x 3 + 0.125313x 4 + 0.017544x 6 + 0.375940x 14 , θ (2) (x) = 0.13805x + 0.40104x 2 + 0.02659x 8 + 0.43433x 16 , θ (3) (x) = 0.139535x + 0.404651x 2 + 0.020930x 8 + 0.434884x 16 ,…”

mentioning

confidence: 99%

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On Mutual Information-Maximizing Quantized Belief Propagation Decoding of LDPC Codes

Cai

Mei

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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A severe problem for mutual informationmaximizing lookup table (MIM-LUT) decoding of low-density parity-check (LDPC) code is the high memory cost for using large tables, while decomposing large tables to small tables deteriorates decoding error performance. In this paper, we propose a method, called mutual information-maximizing quantized belief propagation (MIM-QBP) decoding, to remove the lookup tables used for MIM-LUT decoding. Our method leads to a very practical decoder, namely the MIM-QBP decoder, which can be implemented based only on simple mappings and fixed-point additions. We further present how to practically and systematically design the MIM-QBP decoder for both regular and irregular LDPC codes. Simulation results show that the MIM-QBP decoder can always considerably outperform the state-of-the-art MIM-LUT decoder. Furthermore, the MIM-QBP decoder with only 3 bits per message can outperform the floating-point belief propagation (BP) decoder at high signal-tonoise ratio (SNR) regions when testing on high-rate codes with a maximum of 10-30 iterations.Index Terms-Finite alphabet iterative decoding (FAID), lookup table (LUT), low-density parity-check (LDPC) code, mutual information (MI), quantized belief propagation (QBP).

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Section: B Mim-lut Decoder Design For Regular Ldpc Codesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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On Mutual Information-Maximizing Quantized Belief Propagation Decoding of LDPC Codes

Cai

Mei

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…Notably, the proposed communication system approaches the outage-limit performance over block-fading channels by taking the fading-block length into the design of the modulation strategy and incorporating the unequal error protection feature into the bit-to-symbol mapping scheme. Furthermore, for the sake of review's completeness, it should be mentioned that protograph LDPC codes are also attractive in the field of data storage (see [28] and references therein) and the joint design of source and channel coding on a double protograph is an alternative approach to avoid the breakdown of the end-to-end communication link [29].…”

Section: B Related Workmentioning

confidence: 99%

On Design of Protograph LDPC Codes for Large-Scale MIMO Systems

Vu¹,

Nguyen²,

Nguyen

et al. 2020

IEEE Access

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Protograph low-density parity-check (LDPC) codes and large-scale multi-input multi-output (LS-MIMO) systems have achieved great interest with various practical applications. However, how to effectively evaluate and design protograph LDPC codes for LS-MIMO systems remains a challenging yet critical problem, especially for low-latency applications. To solve that design challenge, the protograph extrinsic information transfer chart (PEXIT) algorithm for LS-MIMO systems, so-called LS-MIMO-PEXIT algorithm, is first derived based on the mutual information functions of messages that are passed on the joint MIMO detection and LDPC decoding graph. The proposed LS-MIMO-PEXIT algorithm plays a vital role in the optimization process of designing new protograph LDPC codes, tailored for LS-MIMO communications systems. Experiment results demonstrate that the analytical results based on the LS-MIMO-PEXIT algorithm are in good agreement with the simulation results under various input constraints, including the coding rate, the number of decoding iterations, and the LS-MIMO configuration. On top of that, the new protograph LDPC codes designed using our LS-MIMO-PEXIT algorithm achieve a coding gain from 0.2 dB at a low coding rate to 0.4 dB at a high coding rate in comparison with the state-of-the-art protograph codes in the literature. Additionally, we incorporate the practical design experience and the theoretical analysis of mutual functions into a two-step procedure to search for protograph LDPC codes that do not have error-floor behavior at frame error rate (FER) or bit error rate (BER) as low as 10 −5 or 10 −7 , respectively. Together with the coding gain, the error-floor-free feature of the proposed protograph LDPC codes is vitally important for future wireless networks where the ultra-reliability is one of the critical requirements. INDEX TERMS Large-scale multiple-input multiple-output (LS-MIMO), protograph low-density paritycheck (LDPC) codes, channel coding, protograph extrinsic information transfer chart, iterative decoding threshold, low latency.

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“…On the other hand, rate-compatible(RC) LDPC is possible to extract increasing parity by parity in accordance with code rate changes. Many research works have proposed the RC LDPC encoding technique to improve the real-time error correction of communication channels where noise can change rapidly in real time [36]- [38], as well as memory systems, such as NAND flash memory [26]- [28], [39]- [41]. The RC LDPC basically extends the matrix depending on the base PCHK.…”

Section: Ldpcs For Flash Memorymentioning

confidence: 99%

A Stepwise Rate-Compatible LDPC and Parity Management in NAND Flash Memory-Based Storage Devices

et al. 2020

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The storage capacity of the NAND flash memory has increased rapidly, and accordingly, the error rate for data writing and reading to the flash memory cell has also escalated. Error-correcting code (ECC) modules, such as low-density parity-check (LDPC), have been applied to flash controllers for error recovery. However, since the error rate increases rapidly, compared to the aging factor and program/erase (P/E) cycle, fixed ECCs and parities are inappropriate methods for resolving this proliferating error, according to the P/E cycle. Therefore, the design of a dynamic ECC scheme and a proper ECC parity management system to increase the lifespan of flash memory storage devices remains in great demand. Herein, an LDPC encoding and decoding scheme is designed to obtain a step-by-step code rate according to the P/E cycle by applying a stepwise rate-compatible LDPC. In addition, an ECC parity management scheme for the increasingly excessive stage-wise ECC is proposed to reduce management and read/write operational overheads. The ECC management scheme also includes the ECC cache system. The proposed LDPC, as well as its management system, will improve the recovery ability of the NAND flash storage device according to the P/E cycle, while it can reduce system read and write overheads due to additional parity data growth.

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Rate-Adaptive Protograph LDPC Codes for Multi-Level-Cell NAND Flash Memory

Cited by 37 publications

References 11 publications

On Mutual Information-Maximizing Quantized Belief Propagation Decoding of LDPC Codes

On Mutual Information-Maximizing Quantized Belief Propagation Decoding of LDPC Codes

On Design of Protograph LDPC Codes for Large-Scale MIMO Systems

A Stepwise Rate-Compatible LDPC and Parity Management in NAND Flash Memory-Based Storage Devices

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