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Low-complexity bound on irregular LDPC belief-propagation decoding thresholds using a Gaussian approximation

Low-complexity bound on irregular LDPC belief-propagation decoding thresholds using a Gaussian approximation

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Since irregular low-density parity-check (LDPC) codes are known to perform better than regular ones, and to exhibit, like them, the so-called ‘threshold phenomenon’, this Letter investigates a low-complexity upper bound on belief-propagation decoding thresholds for this class of codes on memoryless binary input additive white Gaussian noise channels, with sum-product decoding. A simplified analysis of the belief-propagation decoding algorithm is used, i.e. consider a Gaussian approximation for message densities under density evolution, and a simple algorithmic method, defined recently, to estimate the decoding thresholds for regular and irregular LDPC codes.

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