LDPC Check Node Scheduling for Faster Soft Decoding
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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 that involves performing hard decision decoding, determining satisfied and unsatisfied check nodes, and scheduling a check node processing order to prioritize unsatisfied check nodes during soft decision decoding, using a soft log-likelihood ratio generated based on the difference between satisfied and unsatisfied check nodes to improve decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional decoding methods are used to process all check nodes equally, then the decoding process is simple to implement, but the convergence speed is slow and computational complexity is high
Solution Approach 1:
The patent performs a hard decision decoding pass before the soft decision decoding to preliminarily identify which check nodes are satisfied and which are unsatisfied. This preliminary classification allows the subsequent soft decision decoding to prioritize processing of unsatisfied check nodes, thereby improving convergence speed without significantly increasing overall system complexity.
Solution Approach 2:
The patent segments the check nodes into two distinct groups: satisfied check nodes and unsatisfied check nodes. This segmentation is based on the hard decision decoding results and enables differentiated processing strategies for different groups, focusing computational resources on the unsatisfied check nodes that require correction.
2Productivity
If all check nodes are processed in a fixed order during soft decision decoding, then the implementation is straightforward, but the decoding efficiency is low
Solution Approach 1:
The patent implements a dynamic check node processing order that adapts based on the hard decision decoding results. The processing sequence is not fixed but dynamically adjusted to prioritize unsatisfied check nodes, thereby improving decoding efficiency while maintaining reasonable operational complexity through systematic reordering.
Solution Approach 2:
The patent changes the processing parameter of check nodes by reordering them based on their satisfaction status. Unsatisfied check nodes are moved to earlier positions in the processing sequence, while satisfied check nodes are deferred. This parameter change in processing order directly improves decoding efficiency by addressing critical errors first.
3Measurement precision
If soft log-likelihood ratio is generated using only original LLR magnitude, then the hardware constraint is satisfied, but the decoding accuracy is insufficient
Solution Approach 1:
The patent merges multiple factors into the soft log-likelihood ratio generation: the original LLR magnitude, the number of satisfied check nodes, and the number of unsatisfied check nodes. This combination of multiple information sources improves decoding accuracy by incorporating both channel reliability information and code structure information, while the unified formulation maintains hardware implementability.
Solution Approach 2:
The patent incorporates feedback from the hard decision decoding process into the soft decision decoding through the generation of soft log-likelihood ratios. The satisfaction status of check nodes, determined during hard decision decoding, feeds back into the LLR calculation to guide the soft decision decoding more effectively, improving overall decoding accuracy.
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.