Quasi-Cyclic LDPC Block Decoding for Faster Convergence
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Solution Overview
Problem
Current decoding methods for quasi-cyclic Low-Density Parity-Check (LDPC) codes, such as the layered Min-Sum (MS) algorithm, suffer from low decoding efficiency due to high decoding complexity and slow convergence speed, especially in high-code-rate LDPC code parts.
Innovation Solution
The proposed method involves encoding information data using a quasi-cyclic LDPC matrix where the row weight of one row is either greater than or equal to, or less than, the row weight of another row, and preferentially selecting a block matrix with a smaller row weight for iterative decoding, allowing for faster convergence and improved efficiency by performing iterative computation first on rows with smaller weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the layered Min-Sum (MS) algorithm is used for decoding quasi-cyclic LDPC codes, then decoding can be performed with reduced complexity compared to BP, but decoding efficiency remains relatively low due to slow convergence speed
Solution Approach 1:
The check matrix H is divided into multiple block matrices (H1, H2, ..., Hm) based on row weight characteristics. The decoding process is segmented into multiple stages, where different block matrices are processed in sequence according to their row weights. This segmentation allows the decoder to focus computational resources on the most critical parts of the code first, improving overall convergence speed while maintaining the lower complexity advantage of MS algorithm.
Solution Approach 2:
The invention changes the processing order parameter by sorting block matrices according to their row weights. Instead of processing blocks in fixed or random order, the algorithm dynamically adjusts the processing sequence based on row weight parameters, processing blocks with smaller row weights first. This parameter-based reordering optimizes the convergence characteristics of the MS algorithm without increasing its inherent computational complexity.
2Reliability
If iterative decoding is performed on all block matrices regardless of row weight, then complete decoding coverage is achieved, but convergence speed is slow and computing resources are wasted
Solution Approach 1:
Before the main iterative decoding process begins, the algorithm performs preliminary sorting of block matrices based on their row weight parameters. This preliminary action identifies and prioritizes the most critical block matrices that will contribute most to convergence. By preparing this ordered sequence in advance, the decoder ensures that time-sensitive operations are performed in the optimal sequence, achieving both completeness and efficiency.
Solution Approach 2:
The invention maintains continuous useful action by implementing a cascaded decoding structure where block matrices are processed sequentially in order of their row weights. Each completed block processing immediately feeds into the next block in the sequence, ensuring that decoding operations continue without interruption. This continuous processing approach eliminates idle time between operations while maintaining decoding completeness through systematic coverage of all blocks.
Data Source
AI summary
A data transmission method, a sending device, and a receiving device are provided. A sending device obtains information data, encodes the information data by using a quasi-cyclic low-density parity-check (LDPC) code matrix, modulates the encoded data to obtain first data, and sends the first data. A receiving device obtains second data, demodulates the second data to obtain to-be-decoded data, and decodes the to-be-decoded data by using a block matrix in an LDPC code matrix, where the block matrix is a submatrix in the quasi-cyclic LDPC matrix, and in the quasi-cyclic LDPC matrix, a row weight of a row (H−1) is greater than or equal to a row weight of a row H, or a row weight of a row (H−1) is less than or equal to a row weight of a row H. In this way, decoding efficiency can be improved.