Belief propagation LDPC interpretation method based on non-equality information updating

Abstract

The invention discloses a belief propagation LDPC interpretation method based on non-equality information updating. After each iterative judgment of BP algorithm, if next iteration is necessary, the reliability of each variable node is further balanced by using specific judgment rule before the next iteration, and the variable nodes with satisfied preset condition of reliability are selected as reliability variable nodes; when the next iteration is carried out, the current information transferred to the reliability variable nodes, the pseudo-posterior probability of the reliability variable nodes and the current information transferred to adjacent check nodes are not updated any more. Therefore, unreliable probability information is not received from the outside to the reliability variable nodes so that the influence of error message for the reliability variable nodes in the interpretation process is avoided, thereby improving the interpretation performance.

Description

technical field [0001] The invention relates to the technical field of digital information transmission, in particular to a belief propagation LDPC decoding method based on asymmetric message update. Background technique [0002] Low Density Parity Check (LDPC, Low Density Parity Check) code is a linear block code, which can be expressed by generator matrix G and check matrix H, and the number of non-zero elements in its parity check matrix H is far from much less than the number of zero elements. [0003] The decoding algorithm of LDPC code is mainly based on bipartite graph message passing (Message Passing) algorithm, including hard decision decoding algorithm, probability decoding algorithm, sum product algorithm, minimum sum algorithm, bit flipping and large number logic etc. LDPC decoding algorithm code method. [0004] Among them, the Sum Product (Sum Product) algorithm is also called the Belief Propagation (BP, Belief Propagation) algorithm, which is essentially a l...

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Problems solved by technology

[0025] In order to reduce the gap between BP algorithm and MAP decoding performance, some literatures proposed to combine OSD (Ordered Statistic Decoding) into BP algorithm, but the complexity of OSD algorithm is too high, it is difficult to apply code length greater than LDPC code of 500 on

Someone also proposed an augmented BP (Augmented BP) algorithm, which can effectively improve the decoding performance by trying to decode the list, especially when the bit error rate suddenly decreases and enters the Waterfall Region (Waterfall Region). But its implementation structure is too complex

[0026] To sum up, the BP algorithm and its improved algorithm have complex structures, and there is still a performance gap between them and the maximum a posteriori probability decoding.

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