Memory-efficient decoding of LDPC codes

Lee, J.K.-S.; Thorpe, Jeremy

doi:10.1109/isit.2005.1523376

Cited by 58 publications

(59 citation statements)

References 5 publications

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“…To implement LDPC decoders on integrated circuits, all of the messages will be quantized into bits. Existing studies [20] have shown that 4-6 bits' quantization on messages can provide ideal compromise between complexity and performance for LDPC decoders. Among the quantized bits, one is used for the sign, while the rest are used for the magnitude value.…”

Section: Ldpc Decodermentioning

confidence: 99%

“…However, TMR will charge two extra bits for protecting the sign bit while the messages are typically quantized into only 4 to 6 bits [20]. As a result, if we maintain the quantity of message quantization bits under the constraint of complexity, introducing TMR will bring a loss of quantization precision, which is not always beneficial for various storage error ratios.…”

Section: Triple Modular Redundancy Scheme For Sign Bit Protectionmentioning

confidence: 99%

“…However, there are some disadvantages for this protection scheme. Firstly, the adaptive coding is executed inside the single message, which is typically quantized with no more than 7 bits for the reason of complexity [20]. Consequently, there are not enough bits for the efficient coding schemes.…”

Section: Existing Adaptive Messages Coding Schemementioning

confidence: 99%

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Design and Analysis of Adaptive Message Coding on LDPC Decoder with Faulty Storage

Yin

2018

Wireless Communications and Mobile Computing

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Unreliable message storage severely degrades the performance of LDPC decoders. This paper discusses the impacts of message errors on LDPC decoders and schemes improving the robustness. Firstly, we develop a discrete density evolution analysis for faulty LDPC decoders, which indicates that protecting the sign bits of messages is effective enough for finite-precision LDPC decoders. Secondly, we analyze the effects of quantization precision loss for static sign bit protection and propose an embedded dynamic coding scheme by adaptively employing the least significant bits (LSBs) to protect the sign bits. Thirdly, we give a construction of Hamming product code for the adaptive coding and present low complexity decoding algorithms. Theoretic analysis indicates that the proposed scheme outperforms traditional triple modular redundancy (TMR) scheme in decoding both threshold and residual errors, while Monte Carlo simulations show that the performance loss is less than 0.2 dB when the storage error probability varies from 10 −3 to 10 −4 .

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Section: Ldpc Decodermentioning

confidence: 99%

Section: Triple Modular Redundancy Scheme For Sign Bit Protectionmentioning

confidence: 99%

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Design and Analysis of Adaptive Message Coding on LDPC Decoder with Faulty Storage

Yin

2018

Wireless Communications and Mobile Computing

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“…There have been many significant works related to the design and analysis of quantized decoders as well as lowcomplexity implementations of BP-based decoders. Some of the notable works include (but not limited to) the quantized decoders (such as Gallager-E) proposed by Richardson and Urbanke [10], low-complexity BP decoders by Chen et al [20], and by Fossorier, Mihaljevic and Imai [21], quantized BP decoders by Lee and Thorpe [22], and quantized minsum decoders by Smith, Kschischang and Yu [23]. A key distinction that is to be noted between our approach and all these aforementioned works is that their primary objective is to approach BP rather than outperform BP, since they are all based on asymptotic analytical methods.…”

Section: A Quantized Message-passing Decodersmentioning

confidence: 99%

Iterative decoding beyond belief propagation

Planjery¹,

Chilappagari²,

Vasić³

et al. 2010

2010 Information Theory and Applications Workshop (ITA)

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Abstract-At the heart of modern coding theory lies the fact that low-density parity-check (LDPC) codes can be efficiently decoded by belief propagation (BP). The BP is an inference algorithm which operates on a graphical model of a code, and lends itself to low-complexity and high-speed implementations, making it the algorithm of choice in many applications. It has unprecedentedly good error rate performance, so good that when decoded by the BP, LDPC codes approach theoretical limits of channel capacity.However, this capacity approaching property holds only in the asymptotic limit of code length, while codes of practical lengths suffer abrupt performance degradation in the low noise regime known as the error floor phenomenon.Our study of error floor has led to an interesting and surprising finding that it is possible to design iterative decoders which are much simpler yet better than belief propagation! These decoders do not propagate beliefs but a rather different kind of messages that reflect the local structure of the code graph. This has opened a plethora of exciting theoretical problems and applications. This paper introduces this new paradigm.

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“…A large quantization step leads to severe quantization distortion and degrades the performance of decoding, while a small dynamic range yields serious quantization overflow and hence a high error floor. Only non-uniform quantization can balance the requirements between the quantization step and dynamic range [8][9][10] . The optimal non-uniform quantization scheme depends on the distribution of messages, but the distribution varies with the number of iterations.…”

Section: Introductionmentioning

confidence: 99%

Variable non-uniform quantized belief propagation algorithm for LDPC decoding

Liu

Dong

Mei

2008

J. Electron.(China)

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Non-uniform quantization for messages in Low-Density Parity-Check (LDPC) decoding can reduce implementation complexity and mitigate performance loss. But the distribution of messages varies in the iterative decoding. This letter proposes a variable non-uniform quantized Belief Propagation (BP) algorithm. The BP decoding is analyzed by density evolution with Gaussian approximation. Since the probability density of messages can be well approximated by Gaussian distribution, by the unbiased estimation of variance, the distribution of messages can be tracked during the iteration. Thus the non-uniform quantization scheme can be optimized to minimize the distortion. Simulation results show that the variable non-uniform quantization scheme can achieve better error rate performance and faster decoding convergence than the conventional non-uniform quantization and uniform quantization schemes.

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Memory-efficient decoding of LDPC codes

Cited by 58 publications

References 5 publications

Design and Analysis of Adaptive Message Coding on LDPC Decoder with Faulty Storage

Design and Analysis of Adaptive Message Coding on LDPC Decoder with Faulty Storage

Iterative decoding beyond belief propagation

Variable non-uniform quantized belief propagation algorithm for LDPC decoding

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