QC-LDPC Base Matrix Layout for High-Throughput Decoding
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Solution Overview
Problem
Existing 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 method involves creating a base matrix for an irregular QC-LDPC code with specific weight distributions and submatrix structures to enhance decoding efficiency, promoting a 'raptor-like' code structure that supports layered and flooding decoding operations, thereby improving parallelism and reducing stalls during the decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regular QC-LDPC codes are used, then encoding and decoding can be performed efficiently using circulant matrices, but code performance and error correction capability are limited
Solution Approach 1:
The patent applies local quality by creating an irregular QC-LDPC code where different submatrices within the base matrix have different weights and structures. Specifically, the base matrix is divided into submatrices with varying numbers of non-zero elements, allowing certain regions to provide stronger error correction while maintaining overall efficiency. This local differentiation resolves the contradiction by enabling high reliability through irregular structure without requiring complete structural complexity throughout the entire code.
Solution Approach 2:
The patent employs asymmetry by designing a base matrix where rows and columns have unequal weights, and submatrices have asymmetric distributions of non-zero elements. The irregular pattern of circulant matrices creates an asymmetric structure that breaks the symmetry of regular QC-LDPC codes, thereby improving error correction performance while maintaining a manageable level of complexity through the systematic irregular pattern.
2Reliability
If irregular QC-LDPC codes with high-weight base matrices are used, then code quality improves, but decoding complexity and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the base matrix into multiple submatrices, where each submatrix can be processed independently or in parallel during decoding operations. This segmentation allows the decoder to handle high-weight codes by breaking down the complex decoding task into smaller, more manageable units, thereby maintaining high code quality while reducing the practical decoding complexity and resource requirements.
Solution Approach 2:
The patent employs dynamics by implementing rate-adaptable QC-LDPC codes where the base matrix structure can be dynamically configured to match different transmission conditions and channel qualities. This dynamic adaptability allows the system to optimize between code quality and decoding complexity by selecting appropriate base matrix configurations based on current operational requirements, resolving the static contradiction between high code quality and manageable decoding complexity.
3Productivity
If the base matrix has uniform weight distribution, then encoding and decoding are simpler, but throughput and error correction performance are reduced
Solution Approach 1:
The patent applies local quality by creating non-uniform weight distribution in the base matrix where specific submatrices have higher weights tailored to their functional requirements. This local optimization enables certain parts of the code to handle higher throughput demands or provide enhanced error correction, thereby improving overall productivity while managing complexity through targeted rather than universal structural complexity.
4Reliability
If more parity bits are added to increase redundancy for error correction, then reliability improves, but data throughput decreases due to lower coding rate
Solution Approach 1:
The patent employs dynamics by creating rate-adaptable QC-LDPC codes where the base matrix can be dynamically configured to achieve different coding rates. This allows the system to adaptively balance between reliability and throughput by selecting appropriate base matrix configurations based on channel conditions, thereby resolving the static trade-off between adding parity bits for reliability and maintaining data throughput through higher coding rates.
Data Source
AI summary
A base matrix of a rate-adaptive irregular QC-LDPC code is provided, 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 the 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.