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Reduced complexity iterative decoding of low-density parity check codes based on belief propagation

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TLDR
Two simplified versions of the belief propagation algorithm for fast iterative decoding of low-density parity check codes on the additive white Gaussian noise channel are proposed, which greatly simplifies the decoding complexity of belief propagation.
Abstract
Two simplified versions of the belief propagation algorithm for fast iterative decoding of low-density parity check codes on the additive white Gaussian noise channel are proposed. Both versions are implemented with real additions only, which greatly simplifies the decoding complexity of belief propagation in which products of probabilities have to be computed. Also, these two algorithms do not require any knowledge about the channel characteristics. Both algorithms yield a good performance-complexity trade-off and can be efficiently implemented in software as well as in hardware, with possibly quantized received values.

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Citations
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Patent

Decoding architecture for low density parity check codes

TL;DR: In this article, the authors propose an approximation of the standard message passing algorithm used for LDPC decoding, which reduces computational complexity and provides reduced area without substantial added latency by using a block-serial mode.
Journal ArticleDOI

LDPC Decoders with Informed Dynamic Scheduling

TL;DR: Informed Dynamic Scheduling strategies that select the message-passing schedule according to the observed rate of change of the messages are proposed that address several issues including performance for short-blocklength codes, complexity, and implementability.
Patent

Method and apparatus for performing low-density parity-check (ldpc) code operations using a multi-level permutation

TL;DR: In this article, a factorable permuter was used to implement a communications system wherein different devices using the same LDPC code can be implemented using different levels of parallelism. But the use of a novel class of LDPC codes makes such differences in parallelism possible.
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References
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Book

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book

Low-Density Parity-Check Codes

TL;DR: A simple but nonoptimum decoding scheme operating directly from the channel a posteriori probabilities is described and the probability of error using this decoder on a binary symmetric channel is shown to decrease at least exponentially with a root of the block length.
Journal ArticleDOI

Good error-correcting codes based on very sparse matrices

TL;DR: It is proved that sequences of codes exist which, when optimally decoded, achieve information rates up to the Shannon limit, and experimental results for binary-symmetric channels and Gaussian channels demonstrate that practical performance substantially better than that of standard convolutional and concatenated codes can be achieved.
Journal ArticleDOI

A recursive approach to low complexity codes

TL;DR: It is shown that choosing a transmission order for the digits that is appropriate for the graph and the subcodes can give the code excellent burst-error correction abilities.
Journal ArticleDOI

Near Shannon limit performance of low density parity check codes

TL;DR: The authors report the empirical performance of Gallager's low density parity check codes on Gaussian channels, showing that performance substantially better than that of standard convolutional and concatenated codes can be achieved.
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