Orthogonal LDPC Base Graph for Lower-Complexity Parallel Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Low-Density-Parity-Check (LDPC) coding systems, particularly those using quasi-cyclic (QC) parity-check matrices, require significant hardware resources for encoding and decoding, leading to inefficiencies and increased complexity.
Innovation Solution
The implementation of rate-adaptive LDPC codes with orthogonal base graphs and punctured information columns in the extension part of the parity check matrix, reducing conflicts and enabling simpler hardware implementation by processing adjacent rows in parallel, thus improving throughput and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quasi-cyclic (QC) parity-check matrices are used for LDPC coding, then code performance and error correction capability are improved, but hardware resources and system complexity increase significantly
Solution Approach 1:
The parity-check matrix is divided into a base graph that can be systematically extended to generate codes of different rates. This segmentation allows the same base structure to serve multiple coding rates without redesigning the entire matrix, reducing hardware resources while maintaining error correction capability.
Solution Approach 2:
The invention changes the structural parameters of the parity-check matrix by enforcing orthogonality constraints on adjacent rows in the extension part. This parameter change enables more efficient hardware implementation by reducing conflicts and allowing parallel processing, thereby lowering device complexity while preserving reliability.
2Reliability
If QC LDPC encoding and recovery algorithms are implemented, then data protection and transmission reliability are enhanced, but processing time and computational overhead increase
Solution Approach 1:
The base graph structure segments the encoding and decoding processes into modular operations that can be executed in parallel. By organizing the parity-check matrix into a base graph with orthogonal extension parts, the algorithms can process different sections simultaneously, reducing processing time while maintaining data protection.
Solution Approach 2:
The base graph is pre-designed with orthogonal properties that eliminate conflicts before processing begins. This preliminary structural arrangement allows the encoding and recovery algorithms to proceed without computational conflicts, reducing processing time and overhead while maintaining enhanced data protection.
3Device complexity
If adjacent rows in the extension part of the base PCM are made orthogonal, then hardware implementation complexity is reduced and parallel processing is enabled, but code design constraints increase
Solution Approach 1:
The base graph with orthogonal extension parts serves as a universal structure that can generate LDPC codes of multiple rates through systematic extension. This multi-functional design reduces hardware implementation complexity while maintaining code design flexibility, as the same base structure adapts to different coding requirements.
Data Source
AI summary
A decoding method, an encoding method, a decoder and an encoder are disclosed. In an embodiment the decoding method includes receiving, at a receiver of a receiving side, signals from a transmitting side, the signals including a code word and decoding, at a decoder of the receiving side, the code word using a low density parity check (LDPC) code in which each n adjacent rows, n>1, in an extension part of a base parity check matrix (PCM) are orthogonal except for punctured information columns.