QC-LDPC Base Matrix Layout for Parallel Decoding and Code Quality
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Solution Overview
Problem
Current channel coding techniques, particularly Low Density Parity Check (LDPC) codes, face challenges in achieving high data throughput while efficiently managing encoding and decoding resources, especially in maintaining high parallelism and code quality.
Innovation Solution
The method involves creating a base matrix for an irregular QC-LDPC code by dividing rows into high and low-density sets, selecting columns to form orthogonal subrows, and puncturing high-weight columns to enable layered and flooding decoding, thereby achieving high parallelism and maintaining code quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-weight columns are punctured to enable layered decoding, then parallelism during decoding is improved, but code quality may deteriorate
Solution Approach 1:
The base matrix rows are segmented into two sets: high-density rows and low-density rows. This segmentation enables different decoding strategies to be applied to different parts of the matrix, allowing layered decoding to be applied to low-density rows (improving parallelism) while high-density rows maintain code quality through alternative processing.
Solution Approach 2:
Different quality requirements are applied to different parts of the code structure. High-density rows are treated with one decoding approach while low-density rows receive another, allowing each region to be optimized for its specific characteristics. This local differentiation resolves the contradiction by not applying a uniform approach that would compromise overall code quality.
2Productivity
If more columns are selected for orthogonal subrows, then decoding parallelism is improved, but the number of punctured information bits increases
Solution Approach 1:
Instead of puncturing all high-weight columns, only specific columns are selected for puncturing while others are retained. This partial action approach achieves sufficient parallelism improvement without the excessive loss of information that would result from more aggressive puncturing strategies.
3Reliability
If high-density rows are used extensively, then code performance is improved, but decoding complexity increases
Solution Approach 1:
The row set is segmented into high-density and low-density portions, allowing the system to leverage the performance benefits of high-density rows while using low-density rows to reduce overall decoding complexity. This segmentation enables a balanced approach that neither fully exploits nor completely avoids high-density structures.
Data Source
AI summary
Certain aspects of the present disclosure provide an efficiently decodable QC-LDPC code which is based on a base matrix, the base matrix being formed by columns and rows, the columns being dividable into one or more columns corresponding to punctured variable nodes and columns corresponding to non-punctured variable nodes. Apparatus at a transmitting side includes a encoder configured to encode a sequence of information bits based on the base matrix. Apparatus at a receiving side configured to receive a codeword in accordance with a radio technology across a wireless channel. The apparatus at the receiving side includes a decoder configured to decode the codeword based on the base matrix.