LDPC Decoding with Unsatisfied Check Node Prioritization
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Solution Overview
Problem
Existing error-correcting codes, such as LDPC codes, are computationally complex and require improvements in decoding efficiency, particularly in handling unsatisfied check nodes during the decoding process.
Innovation Solution
A method involving hard decision decoding followed by soft decision decoding, where the check node processing order is scheduled to prioritize unsatisfied check nodes, with soft log-likelihood ratios generated based on the difference between satisfied and unsatisfied check nodes, and updated after each iteration to enhance decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error-correcting codes (Hamming, Reed-Solomon, Turbo, LDPC) are used, then data corruption can be corrected, but the computation complexity increases
Solution Approach 1:
The patent performs a hard decision decoding as a preliminary step before soft decision decoding. This preliminary action identifies satisfied and unsatisfied check nodes in advance, allowing the subsequent soft decision decoding to focus computational resources only on unsatisfied check nodes, thereby reducing overall computation complexity while maintaining error correction capability
Solution Approach 2:
The patent segments the decoding process into two distinct phases: hard decision decoding and soft decision decoding. It further segments the check nodes into satisfied and unsatisfied groups, processing them differently. This segmentation allows the system to handle the computationally intensive soft decision decoding only for unsatisfied check nodes, reducing overall computational complexity
2Measurement precision
If soft decision decoding is performed on all check nodes, then decoding accuracy improves, but processing time increases
Solution Approach 1:
The patent extracts and identifies satisfied check nodes from the complete set of check nodes after hard decision decoding. These satisfied check nodes are then excluded from further soft decision decoding processing. Only the unsatisfied check nodes undergo soft decision decoding, which reduces processing time while maintaining decoding accuracy for the critical error-prone portions
Solution Approach 2:
The hard decision decoding serves as a preliminary filtering step that identifies which check nodes require further soft decision decoding. This preliminary classification allows the system to skip soft decision decoding for satisfied check nodes, significantly reducing processing time while maintaining accuracy for unsatisfied check nodes
Data Source
AI summary
Systems and methods are provided for decoding a codeword encoded by a linear block code. A method may comprise performing a hard decision decoding on a codeword, determining which check nodes are satisfied and which check nodes are unsatisfied after the hard decision decoding, scheduling a check node processing order by moving at least one unsatisfied check node to be processed ahead of at least one satisfied check node and performing a soft decision decoding on the codeword according to the check node processing order.