Optimal Output Quantization of Binary Input AWGN Channel for Belief-Propagation Decoding of LDPC Codes

Cho, Junho; Kim, Jonghong; Sung, Wonyong

doi:10.1109/sips.2012.51

Cited by 3 publications

(10 citation statements)

References 11 publications

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“…Our study differs from [6] by the use of finite-precision LDPC decoders where the messages exchanged using density evolution are also quantized on a fixed number of bits as well as the decoder input. Moreover, our study includes both regular and irregular LDPC codes.…”

Section: A Decision Levels Quantizer (Dl)mentioning

confidence: 99%

“…There are few works in literature that analyze the dependency of optimal quantizers on the channel code. In [6], the authors consider the code-dependent quantizers for BI-AWGN channel when regular LDPC codes are used and evaluate the quantizer performance by density evolution. They demonstrate that quantizers that maximize the noise threshold are superior to Lloyd quantizers.…”

Section: Introductionmentioning

confidence: 99%

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Code-aware quantizer design for finite-precision min-sum decoders

Mheich

Nguyen-Ly

Savin

et al. 2016

2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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Abstract-Classically, the quantization of the soft information supplied to a finite-precision decoder is chosen to optimize a certain criterion which does not depend on the characteristics of the existing code. This work studies code-aware quantizers, for finite-precision min-sum decoders, which optimize the noise threshold of the existing family of Low-Density Parity-Check (LDPC) codes. We propose a code-aware quantizer with lower complexity than that obtained by optimizing all decision levels and approaching its performance, for few quantization bits. We show that code-aware quantizers outperform code-independent quantizers in terms of noise threshold for both regular and irregular LDPC codes. To overcome the error floor behavior of LDPC codes, we propose the design of the quantizer for a target error probability at the decoder output. The results show that the quantizer optimized to get a zero error probability could lead to a very bad performance for practical range of signal to noise ratios. Finally, we propose to design jointly irregular LDPC codes and code-aware quantizers for finite-precision min-sum decoders. We show that they achieve significant decoding gains with respect to LDPC codes designed for infinite-precision belief propagation decoding, but decoded by finite-precision min-sum.

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Section: A Decision Levels Quantizer (Dl)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Code-aware quantizer design for finite-precision min-sum decoders

Mheich

Nguyen-Ly

Savin

et al. 2016

2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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“…Design of optimum output quantizers for BI-AWGN channel under BP decoding was studied in [7]. Consider an independent and identically distributed Gaussian random variable ‫ݖ‬ with zero-mean and variance ߪ ଶ .…”

Section: A Output Quantization Of Bi-awgn Channelmentioning

confidence: 99%

“…However, it still offers good error performance when belief-propagation (BP) algorithm [6] with soft-decision information is used for the decoding process. Meanwhile, the error performance of LDPC decoding is closely related to the precision of log-likelihood ratio (LLR) information [7]. In order to increase the precision of LLR information, multiple voltage sensing operations are required because only a simple quantization circuit is used inside in NAND flash memory.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal output quantizer proposed in [7] is used for determining the threshold levels of the BI-AWGN channel, while the non-uniform quantization scheme proposed in [8] is adopted for the NAND Flash memory channel. Note that LLR computation of Flash memory channel follows the method in [7]. The purpose of this work is not only to assess the error performan- ce when the signal is quantized but also to know the effects of input signal distribution.…”

Section: Introductionmentioning

confidence: 99%

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Performance of rate 0.96 (68254, 65536) EG-LDPC code for NAND Flash memory error correction

Kim

Lee

Sung

2012

2012 IEEE International Conference on Communications (ICC)

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As the process technology scales down and the number of bits per cell increases, NAND Flash memory is more prone to bit errors. In this paper, we employ a rate-0.96 (68254, 65536) Euclidean geometry (EG) low-density parity-check (LDPC) code for NAND Flash memory error correction, and evaluate the performance under binary input (BI) additive white Gaussian noise (AWGN) and NAND Flash memory channels. The performance effect of output signal quantization is also studied. We show the strategies for determining the optimum quantization boundaries and computing the quantized loglikelihood ratio (LLR) for the NAND Flash channel model that is approximated as a mixture of Gaussian distributions. Simulation results show that the error performance with the NAND Flash memory channel is much different from that with the BI-AWGN channel. Since the distribution of NAND Flash memory output signal is not stationary, it is important to accurately assess the stochastic distribution of the signal for optimum sensing.

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Adaptive Quantization Scheme for Multi-Level Cell NAND Flash Memory

Lee

Sung

2013

The Journal of Korean Institute of Communications and Informati

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Optimal Output Quantization of Binary Input AWGN Channel for Belief-Propagation Decoding of LDPC Codes

Cited by 3 publications

References 11 publications

Code-aware quantizer design for finite-precision min-sum decoders

Code-aware quantizer design for finite-precision min-sum decoders

Performance of rate 0.96 (68254, 65536) EG-LDPC code for NAND Flash memory error correction

Adaptive Quantization Scheme for Multi-Level Cell NAND Flash Memory

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