LDPC Matrix Structure for Flexible Code Length and Rate
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Solution Overview
Problem
Existing LDPC codes struggle to support flexible code length and rate requirements, limiting their applicability in various communication systems.
Innovation Solution
A novel LDPC matrix structure with specific submatrices A and B, along with optional submatrices C, D, and E, allows for flexible code length and rate adjustments through lifting factors Z and permutations, enabling efficient encoding and decoding of information sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional LDPC codes are used, then error correction capability is provided, but flexible code length and rate requirements cannot be met
Solution Approach 1:
The LDPC matrix is divided into multiple submatrices (A, B, C, D, E) with specific structures. Submatrix A is a 5x22 matrix with specific column weights, submatrix B is a 5x5 matrix with bi-diagonal structure, and submatrices C, D, E are optional. This segmentation allows flexible configuration of code length and rate while maintaining reliable error correction through the structured submatrix design.
2Productivity
If LDPC matrix with special structure is used, then throughput is improved, but code length and rate flexibility is reduced
Solution Approach 1:
The LDPC matrix structure incorporates optional submatrices C, D, and E that can be dynamically configured based on code length and rate requirements. The base matrix structure with submatrices A and B provides the foundation for high throughput, while the optional submatrices enable dynamic adaptation to different communication scenarios, achieving both high productivity and versatility.
3Reliability
If fixed LDPC matrix structure is used, then encoding and decoding performance is stable, but error floor cannot be reduced
Solution Approach 1:
The LDPC matrix employs local quality optimization through specifically designed submatrices. Submatrix A has columns with weights of 5, 4, and 3 to optimize local error correction capability. Submatrix B has a bi-diagonal structure with columns of weights 3, 2, and 1. These localized structural optimizations reduce the error floor while maintaining overall performance stability.
Data Source
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AI summary
This application discloses an encoding method, an apparatus, a communications device, and a communications system. The method includes: encoding an input bit sequence by using a low-density parity-check LDPC matrix, where a base graph of the LDPC matrix is represented by a matrix of m rows and n columns, m is an integer greater than or equal to 5, and n is an integer greater than or equal to 27; the base graph includes at least a submatrix A and a submatrix B; the submatrix A is a matrix of five rows and 22 columns; and the submatrix B is a matrix of five rows and five columns, and the submatrix B includes a column whose weight is 3 and a submatrix B' with a bidiagonal structure. According to the encoding method, the apparatus, the communications device, and the communications system in this application, encoding requirements of information bit sequences of a plurality of lengths can be supported.