Multi-value corrected min-sum decoding method applicable to low-density parity-check code

Abstract

The invention discloses a multi-value corrected min-sum decoding method applicable to a low-density parity-check code. The method comprises the following steps of: for each check node, multiplying signs of data input by associated variable nodes to acquire a total output sign, comparing absolute values of the data input by the adjacent variable nodes to acquire a minimum value, a second minimum value and a third minimum value, and recording a sequence number of a variable node corresponding to the minimum value; for the variable node corresponding to the minimum value, performing addition operation in a log likelihood ratio (LLR) domain on the second minimum value and the third minimum value, and multiplying the acquired result with the total output sign and the sign of the data input by the variable node to acquire information output to the variable node corresponding to the minimum value; and for other variable nodes, performing the addition operation in the LLR domain on the minimum value and the third minimum value, and multiplying the acquired result with the total output sign and the signs of the data input by the variable nodes to acquire information output to the variable nodes. The decoding method is high in performance, low in complexity and easy to implement.

Description

technical field [0001] The invention relates to an iterative decoding method of a Low Density Parity Check (LDPC) code, and belongs to the technical field of channel error correction coding decoding. Background technique [0002] In various communication systems, transmission bits are affected by channel random noise and generate random errors. It has been proved by theory and practice that the error-correcting coding method, which provides transmission reliability by introducing redundancy, is a kind of effective means. The Turbo code and the Low-Density Parity-Check (LDPC for short) code introduced in recent years are one of the coding schemes with the strongest error correction capability found so far. [0003] Compared with Turbo codes, the design of LDPC codes is more flexible (different code rates / code lengths), and the fully parallel structure of the LDPC code decoding algorithm makes it easier to design high-throughput LDPC decoders. Therefore, the standardization ...

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Problems solved by technology

[0006] These two improved MSA algorithms are based on the original algorithm and introduce correction factors. What they have in common with the original MSA algorithm is that they only use the minimum and sub-minimum values ​​of the input information of variable nodes, so they do not use relatively Many variable nodes input information, resulting in a certain gap between the performance of these algorithms and the SPA algorithm

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