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 ...
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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-propagationdecoding 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.
In this study, an efficient decodingalgorithm is proposed to decode non-binary low-density parity-check (LDPC) codes. The algorithm is derived from the belief-propagation (BP) decoding with genetic algorithm of binar...
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In this study, an efficient decodingalgorithm is proposed to decode non-binary low-density parity-check (LDPC) codes. The algorithm is derived from the belief-propagation (BP) decoding with genetic algorithm of binary LDPC codes. For the proposed algorithm, two decoding constraints are introduced to determine variable nodes which are considered highly reliable. The messages from these highly reliable variable nodes are then magnified with an appropriate parameter . This process can make the messages propagate in the Tanner graph more efficiently. Simulation results show that, compared with the fast Fourier transform-based BP algorithm, the proposed algorithm can have an equal or better performance in low bit-error-rate region over both the additive white Gaussian noise channels and the mobile fading channels.
This paper proposes a simplified forced-convergence (SFC) algorithm to reduce the computational complexity for low-density parity-check (LDPC) decoding. To reduce the computational complexity, the proposed algorithm u...
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ISBN:
(纸本)9781479952304
This paper proposes a simplified forced-convergence (SFC) algorithm to reduce the computational complexity for low-density parity-check (LDPC) decoding. To reduce the computational complexity, the proposed algorithm uses the modified check node (CN) operation that does not use a condition for deactivating CNs. Therefore, the proposed SFC algorithm uses only one threshold value while the existing forced-convergence (FC) algorithm uses two threshold values. The simulation results show that SFC achieves a bit error rate (BER) performance close to min-sum (MS) algorithm. SFC can reduce the computational complexity of check node by approximately 22.21% compared to FC.
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