Irregular QC-LDPC Base Matrix for Hybrid Layered Decoding
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Solution Overview
Problem
Current channel coding methods face challenges in achieving high data throughput while efficiently managing encoding and decoding resources, particularly in noisy communication channels.
Innovation Solution
The development of an irregular Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) code structure with a base matrix that prioritizes higher weight columns and rows, allowing for layered decoding and reducing stalls during the decoding process, which enhances decoding efficiency and promotes a 'raptor-like' code structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If regular QC-LDPC code structure is used, then encoding and decoding can be performed efficiently, but decoding stalls occur and throughput is limited
Solution Approach 1:
The patent applies local quality by creating an irregular QC-LDPC code structure where different columns and rows have different weights (numbers of non-zero elements). Specifically, some columns have higher weight than others, and some rows have higher weight than others. This non-uniform distribution of weights across different parts of the parity-check matrix enables certain columns to be processed more efficiently during decoding while maintaining overall code performance, thereby resolving the contradiction between throughput and error rate.
2Productivity
If higher weight columns and rows are prioritized in the base matrix, then decoding efficiency improves and stalls are reduced, but code structure complexity increases
Solution Approach 1:
The patent segments the parity-check matrix into distinct high-weight and low-weight columns and rows, creating a structured irregular pattern. The base matrix is designed with specific segments that have higher weights, and this structure is then expanded to form the full QC-LDPC code. This segmentation approach improves decoding efficiency by allowing prioritized processing of high-weight segments while maintaining a systematic construction method that manages complexity.
Solution Approach 2:
The patent employs dynamic elements in the base matrix construction, where the positions and weights of non-zero elements are strategically varied across different columns and rows. This dynamic structure allows the code to adapt its decoding behavior - high-weight columns can be processed with more iterations or different algorithms, while low-weight columns use standard processing, thereby improving overall efficiency without uniformly increasing complexity across the entire code structure.
3Reliability
If irregular QC-LDPC code with differentiated column and row weights is implemented, then decoding performance improves, but encoding and decoding resource management becomes more difficult
Solution Approach 1:
The patent achieves universality by designing a base matrix structure that can generate multiple irregular QC-LDPC codes with different rates and properties from a single systematic construction approach. The base matrix with its differentiated column and row weights serves as a universal template that can be expanded and configured for various applications, allowing the same fundamental structure to provide both high code quality and manageable resource requirements across different scenarios.
Data Source
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AI summary
Provided is a base matrix of a rate-adaptive irregular QC-LDPC code, the base matrix being formed by columns and rows having entries representing circulant submatrices. The columns of the base matrix are divided into at least one or more higher weight first columns and lower weight second columns and the rows of the base matrix are divided into first high weight rows corresponding to the high rate mother code and second low weight rows corresponding to the extension part related to lower rate codes. A first submatrix formed by an intersection of entries of the second columns and entries of the first and the second rows is divided into first quadratic submatrices, wherein at most one entry in each column of each first submatrix and/or at most one entry in each row of each first submatrix is labelled thereby dividing the first submatrix into layers of groups of orthogonally labelled rows, wherein the number of orthogonally labelled rows corresponds to the size of the first quadratic submatrices. One or more check node unit processors perform flooding or a combination of flooding and layered (hence hybrid) decoding operations in view of the one or more higher weight first columns and one or more CNU processors perform layered decoding operations in view of the second columns.