Density evolution thresholds for noise-against-noise min-sum decoders

In this paper, we define Noise-against-Noise Min-Sum (NAN-MS) decoders as decoders that incorporate a certain amount of random perturbation due to deliberate noise injection. We introduce a noise model which is used to implement quantized NAN-MS decoders, using a limited number of precision bits. Th...

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Bibliographic Details
Main Authors: Cochachin, F., Declercq, D., Boutillon, E., Kessal, L.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Decoding
Iterative decoding
Noise measurement
Probabilistic logic
Probability density function
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Summary:In this paper, we define Noise-against-Noise Min-Sum (NAN-MS) decoders as decoders that incorporate a certain amount of random perturbation due to deliberate noise injection. We introduce a noise model which is used to implement quantized NAN-MS decoders, using a limited number of precision bits. The behavior of NAN-MS decoders is investigated in the asymptotic limit of the code length using a noisy version of density evolution (DE). We use the noisy-DE thresholds to analyze and optimize the noise model parameters. We show that a controlled injection of noise allows NAN-MS decoders to achieve better performance than noiseless MS decoders, especially for low precision. The finite-length simulations confirm the conclusions of the DE analysis.
ISSN:2166-9589
DOI:10.1109/PIMRC.2017.8292326