LDPC decoding method for improving minimum offset sum based on density evolution

Abstract

The invention relates to the technical field of mobile communication, in particular to an LDPC decoding method for improving minimum offset sum based on density evolution. The method comprises the following steps of calculating a channel message value received by a variable node; updating a check node and the variable node, and updating the external information transmitted between the check node and the variable node by using an optimal offset factor value; calculating a hard decision value of the variable node, and performing hard decision; and judging whether the hard decision meets a checkmatrix, and if so, outputting the hard decision as a decoding result. According to the method, the LLR message values before and after variable node updating are used for weighting in the number domain, so that the oscillation of the LLR information of the variable node during the decoding process is relieved, and the decoding convergence speed is increased; a density evolution method and the weighted average are adopted to calculate the offset factors under each signal-to-noise ratio, the linear approximation processing is combined to obtain the optimal offset factor, and the optimal offset factor is used as a constant to be stored in hardware, so that the redundant hardware calculation consumption is not increased during the implementation process.

Description

technical field [0001] The invention relates to the technical field of mobile communication, in particular to an LDPC decoding method based on density evolution to improve the minimum sum of offsets. Background technique [0002] Low Density Parity Check Code (Low Density Parity Check Code, LDPC) is a linear block error correction code with low decoding complexity and excellent performance. The transfer algorithm has error correction performance close to the Shannon limit when decoding. At present, LDPC has been widely used in deep space communication, optical fiber communication, satellite digital video and audio broadcasting, and has been adopted by various modern communication standards. [0003] The traditional LDPC decoding algorithm has limitations in improving its decoding performance in some cases. For example, taking the inspection matrix represented by the tanner graph as an example, when there is no cycle on the tanner graph, the existing decoding method can achie...

AI Technical Summary

Problems solved by technology

[0003] The traditional LDPC decoding algorithm has limitations in improving its decoding performance in some cases. For example, taking the inspection matrix represented by the tanner graph as an example, when there is no cycle on the tanner graph, the existing decoding method can achieve the best Decoding, and when there is a ring on the tanner graph (the ring refers to the path from a node to the same node without repeating the edge, that is, when there is a ring, the information will be repeatedly transmitted between the two groups of nodes, and it is difficult to update, which violates the iterative translation method. The original intention of the code) especially when the ring is small, the positive feedback effect of iterative decoding will not be obvious, and there will be problems in the judgment message, which will lead to wrong judgments and reduce the decoding performance. Especially when the SNR is medium or high, the existence of the ring will Causes the external information transmitted by the variable node and the check node to oscillate, reducing the performance of the decoding algorithm

Existing simplified algorithms that are easy to implement in hardware, such as the normalized minimum sum algorithm and other improved minimum sum algorithms, mainly consider how to improve the decoding performance by correcting the information amplitude or updating the variable node information multiple times, without considering the tanner The Influence of External Message Oscillation Phenomenon on Decoding When There Are Rings on Graph

Images