Structured LDPC Base Matrix for Flexible HARQ Code Rates
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Solution Overview
Problem
Structured LDPC codes in existing communication systems cannot support incremental redundancy HARQ and have insufficient flexibility, leading to limitations in ultra-high throughput and code rate flexibility, which are essential for advanced communication standards like 5G.
Innovation Solution
The proposed solution involves designing a structured LDPC encoding and decoding method using a specific base matrix structure with submatrices, including upper-left submatrices Hb1 and Hb2, where the number of rows and columns are reduced, and an expansion factor Z is used to support various code rates and lengths, enabling efficient LDPC encoding and decoding with performance comparable to turbo codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a structured LDPC code is used, then decoding complexity is reduced and hardware implementation is simplified, but the code cannot support incremental redundancy HARQ and has insufficient flexibility in code rates and lengths
Solution Approach 1:
The base matrix is divided into multiple submatrices (first submatrix, second submatrix, third submatrix, fourth submatrix) with specific structural characteristics. This segmentation allows different parts of the matrix to serve different functions: some submatrices support the girth constraint for decoding performance, while others enable incremental redundancy HARQ and provide flexibility in code rates and lengths.
Solution Approach 2:
The patent introduces a dynamic structure where the base matrix can be configured with different submatrix arrangements and parameters (such as different girth values, different submatrix sizes) to adapt to various code rates and lengths. This dynamic configurability enables the structured LDPC code to support incremental redundancy HARQ while maintaining low decoding complexity through the preserved sparse structure.
2Adaptability or versatility
If the base matrix is designed with multiple submatrices to support incremental redundancy HARQ, then adaptability is improved, but device complexity increases
Solution Approach 1:
The base matrix is segmented into four submatrices with specific structural characteristics. This segmentation enables the matrix to support incremental redundancy HARQ functionality while maintaining a regular sparse structure that can be efficiently implemented in hardware. Each submatrix can be processed independently, reducing overall hardware complexity compared to a fully irregular matrix.
Solution Approach 2:
Different submatrices are assigned different local structures and properties. For example, some submatrices may have higher density to support HARQ functionality, while others maintain sparsity for efficient decoding. This local differentiation allows the overall matrix to achieve high adaptability without uniformly increasing complexity across the entire structure.
3Reliability
If the girth of the base matrix is increased to improve decoding performance, then reliability is improved, but the number of possible base matrices is reduced
Solution Approach 1:
The base matrix is divided into submatrices, allowing the girth constraint to be applied locally to specific submatrices rather than requiring the entire matrix to have high girth. This segmentation enables decoding performance improvement through localized girth control while maintaining flexibility in the overall matrix design and preserving a larger number of possible base matrix configurations.
Solution Approach 2:
The girth property is optimized locally within specific submatrices rather than uniformly across the entire base matrix. This allows certain submatrices to have higher girth for improved decoding performance in critical areas, while other submatrices can have more flexible structures that increase the overall number of possible base matrix configurations.
Data Source
AI summary
Provided is an encoding method and device and a decoding method and device for structured LDPC. The encoding method includes: determining a base matrix used for encoding and performing an LDPC encoding operation on a source information bit sequence according to the base matrix and an expansion factor Z corresponding to the base matrix to obtain a codeword sequence, where Z is a positive integer. The base matrix includes multiple submatrices and the submatrices include an upper-left submatrix Hb1 and an upper-left submatrix Hb2, and the upper-left submatrix Hb1 is an upper-left submatrix of the upper-left submatrix Hb2.