Optimization of Protograph LDPC Codes based on High-Level Energy Models

Yaoumi, Mohamed; Leduc-Primeau, François; Dupraz, Elsa; Guilloud, Frédéric

doi:10.1109/iswcs.2019.8877353

Cited by 3 publications

(12 citation statements)

References 19 publications

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“…N (ξ) = 0.0049 at ξ = 1.45dB. In Table I, we also give the protographs S m and S c which were obtained in [5]. We now compare the performance of these three protographs in terms of energy and performance.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The first model counts the number of memory writes in the decoder (memory model), and the second model counts the number of operations realized by the decoder (complexity model). These two models were first given in [5], and we update them with the evaluation of the average number of iterations introduced in Section III. In order to express these models, we defined Therefore, at SNR ξ, the memory energy model is given by…”

Section: Energy Modelsmentioning

confidence: 99%

“…However, [6] considers hard-decision Gallager B decoders with poor performance, and [4] considers infinite precision sum-product decoding algorithms which cannot be implemented directly on hardware. To circumvent these issues, in [5], we proposed two models to evaluate the energy consumption of a more practical quantized Min-Sum decoder [7]. The first model evaluates the energy consumption from the number of operations realized in the decoder, and the second model counts the number of memory writes in the decoder.…”

mentioning

confidence: 99%

“…In this paper, we also study the quantized Min-sum decoder of [7], and consider Quasy-Cyclic (QC) codes constructed from protographs, for their easy hardware implementation. We rely on the two energy models introduced in [5]. Since these two models depend on the average number of iterations performed in the decoder, we introduce a method to precisely evaluate this number, depending on the codeword length and on the considered protograph.…”

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confidence: 99%

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Energy optimization of quantized min-sum decoders for protograph-based LDPC codes

et al. 2020

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This paper considers protograph-based LDPC codes, and proposes an optimization method to select protographs that minimize the energy consumption of quantized Min-Sum decoders. This method first estimates the average number of iterations required by the decoder, and includes this estimate into two high level models that evaluate the decoder energy consumption. The optimization problem is then formulated as minimizing the energy consumption of the decoder while satisfying a performance criterion on the Frame Error Rate. Finally, an optimization algorithm based on differential evolution is introduced. Protograph optimized for energy consumption shows a gain in energy of approximately 15% compared to a baseline protograph optimized for performance only. I. INTRODUCTION Reducing the energy consumption of telecommunication systems may allow to improve the communication capabilities of systems with limited resources. This energy consumption comes for a large part from the transmission power of the emitter, but the processing power at the receiver is also non-negligible for short-length communications [1]. In addition, error-correction decoders are known to use a large part of this processing power. Therefore, the objective of this paper is to reduce the energy consumption of the error-correction part. It considers Low Density Parity Check (LDPC) codes as a particular family of error-correction codes which were retained in the 5G standardization process. The problem of reducing the energy consumption of LDPC decoders has received increased attention recently. In [2], the energy consumption of hard-decision LDPC decoders is estimated from the number of computation operations realized in the decoder, and from the average length

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Energy Modelsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Energy optimization of quantized min-sum decoders for protograph-based LDPC codes

et al. 2020

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“…This method allows to estimate the finite-length performance of an LDPC decoder, from a formula that depends on the asymptotic error probabilities provided by DE. Although this method does not take into account the effect of cycles, [16], [17], show that it accurately predicts the decoder performance measured from Monte Carlo simulations, for long enough codewords. The formula proposed in [16] can be straightforwardly used in our case, by using in the formula the asymptotic error probabilities Figure 1 provides the BERs predicted by the FL-DE method, and the BERs measured from Monte Carlo simulations.…”

Section: B Fl-de Analysismentioning

confidence: 95%

Self-Corrected Belief-Propagation Decoder for Source Coding With Unknown Source Statistics

Dupraz

Yaoumi

2021

IEEE Commun. Lett.

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This paper describes a practical Slepian-Wolf source coding scheme based on Low Density Parity Check (LDPC) codes. It considers the realistic setup where the parameters of the statistical model between the source and the side information are unknown. A novel Self-Corrected Belief-Propagation (SC-BP) algorithm is proposed in order to make the coding scheme robust to incorrect model parameters by introducing some memory inside the LDPC decoder. A Two Dimensional Density Evolution (2D-DE) analysis is then developed to predict the theoretical performance of the SC-BP decoder. Both the 2D-DE analysis and Monte-Carlo simulations confirm the robustness of the SC-BP decoder. The proposed solution allows for an important complexity reduction and shows a performance very close to existing methods which jointly estimate the model parameters and the source sequence.

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Energy Optimization of Faulty Quantized Min-Sum LDPC Decoders

Yaoumi,

Nadal,

Dupraz

et al. 2021

Preprint

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The objective of this paper is to minimize the energy consumption of a quantized Min-Sum LDPC decoder, by considering aggressive voltage downscaling of the decoder circuit. Since low power supply may introduce faults in the memories used by the decoder architecture, this paper proposes to optimize the energy consumption of the faulty Min-Sum decoder while satisfying a given performance criterion.The proposed optimization method relies on a coordinate descent algorithm that optimizes code and decoder parameters which have a strong influence on the decoder energy consumption: codeword length, number of quantization bits, and failure probability of the memories. Optimal parameter values are provided for several codes defined by their protographs, and significant energy gains are observed compared to non-optimized setups.

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Optimization of Protograph LDPC Codes based on High-Level Energy Models

Cited by 3 publications

References 19 publications

Energy optimization of quantized min-sum decoders for protograph-based LDPC codes

Energy optimization of quantized min-sum decoders for protograph-based LDPC codes

Self-Corrected Belief-Propagation Decoder for Source Coding With Unknown Source Statistics

Energy Optimization of Faulty Quantized Min-Sum LDPC Decoders

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