Structured LDPC Encoding and Decoding With Reduced Storage Load
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Low Density Parity Check (LDPC) codes are not widely deployed due to their complexity in encoding and decoding processes, high storage requirements, and computational load, particularly in check node operations, which hinders efficient implementation and high data rate support.
Innovation Solution
The method involves structured LDPC code encoding and decoding using a parity check matrix organized in tabular form, with parity bit accumulators initialized and operated based on specific operations to simplify the encoding and decoding processes, reducing storage needs and improving communication between processing nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LDPC encoding is performed using generator matrix, then encoding capability is achieved, but storage requirements become very large due to non-sparse matrix
Solution Approach 1:
The parity check matrix is divided into multiple sub-matrices arranged in a specific structure. Each sub-matrix can be processed independently, allowing the large matrix to be stored and manipulated in a distributed manner, reducing the storage burden on any single component while maintaining the overall encoding capability.
Solution Approach 2:
The patent exploits the sparse property of LDPC matrices by storing only the non-zero elements and their positions. This local quality approach means that instead of storing the entire large matrix with many zeros, only the essential non-zero elements are stored, significantly reducing storage requirements while preserving encoding functionality.
2Reliability
If LDPC codes use large blocks to be effective, then error correction performance improves, but storage and processing complexity increases
Solution Approach 1:
The large parity check matrix is segmented into smaller sub-matrices with a structured arrangement. This segmentation allows the system to handle large block codes by processing them in manageable pieces, reducing the complexity of storage and processing while maintaining the error correction performance benefits of large blocks.
Solution Approach 2:
The patent pre-organizes the parity check matrix into a specific structured form with predetermined patterns. This preliminary organization allows for more efficient processing during encoding and decoding operations, as the structure is already optimized before the actual data processing begins, reducing runtime complexity.
3Measurement precision
If check node operations are performed in LDPC decoding, then decoding accuracy improves, but computational load increases
Solution Approach 1:
The decoding process is divided into multiple iterations and stages, with check node operations performed in a structured sequence. This segmentation allows the computational load to be distributed over time and across different processing units, making the high computational requirements more manageable while maintaining decoding accuracy through multiple passes.
Solution Approach 2:
The LDPC decoding employs iterative check node operations that are repeated in a periodic manner. Each iteration refines the decoding accuracy, and the periodic structure allows for efficient resource utilization and load management, balancing computational requirements with performance goals.
4Quantity of substance
If structured parity check matrix is used, then storage requirements are reduced, but encoding complexity may increase
Solution Approach 1:
The structured parity check matrix is designed with local patterns and regularities that can be exploited during encoding. By organizing the matrix with specific structural properties, the patent reduces storage requirements while the local patterns enable efficient encoding algorithms that do not significantly increase complexity.
Solution Approach 2:
The parity check matrix is pre-structured into a specific form with predetermined patterns and relationships between sub-matrices. This preliminary structuring allows encoding operations to follow systematic procedures rather than requiring complex ad-hoc calculations, actually simplifying the encoding process despite the structured format.
Data Source
AI summary
An approach is provided for processing structure Low Density Parity Check (LDPC) codes. Memory storing edge information and a posteriori probability information associated with a structured parity check matrix used to generate Low Density Parity Check (LDPC) coded signal are accessed. The edge information represent relationship between bit nodes and check nodes, and are stored according to a predetermined scheme that permits concurrent retrieval of a set of the edge information.