Adaptive LDPC Decoding Rules for Lower Error Floor
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Solution Overview
Problem
Low density parity check (LDPC) error correction methods suffer from high complexity and an increased error floor due to approximation, which limits their effectiveness in improving signal-to-noise ratio (SNR) and bit-error rate (BER) performance.
Innovation Solution
An error correction circuit and method that dynamically adjusts decoding parameters based on error rates by performing repeated LDPC decoding operations, focusing on variable nodes with different graph-degrees to optimize decoding rules and output corrected data, thereby reducing the error floor and improving BER with increasing SNR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LDPC decoding is performed with high calculation complexity to achieve low error rate, then bit-error rate performance improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the decoding process by dividing variable nodes into groups based on their graph-degree values. Different decoding rules are applied to different segments (groups of variable nodes with same graph-degree), allowing the system to manage complexity through structured division while maintaining effective error correction across all node types.
Solution Approach 2:
The patent applies local quality by using different decoding rules tailored to specific groups of variable nodes based on their graph-degree characteristics. Each group receives a decoding rule optimized for its specific properties, rather than applying a uniform rule to all nodes, thereby improving overall decoding performance while managing complexity.
2Productivity
If approximation methods are used to reduce LDPC calculation complexity, then processing speed improves, but error floor increases
Solution Approach 1:
The patent changes parameters by systematically varying decoding rules based on graph-degree values of variable nodes. Instead of using a fixed approximation rule for all nodes, the system adjusts decoding parameters (rules) according to the specific graph-degree characteristics of each variable node group, thereby maintaining accuracy while enabling efficient processing.
Solution Approach 2:
The patent introduces dynamics by making the decoding rule selection adaptive rather than static. The system dynamically selects appropriate decoding rules based on the graph-degree values of variable nodes, allowing the decoding process to adapt to different node characteristics and avoid the error floor problem associated with fixed approximation methods.
3Device complexity
If uniform decoding rules are applied to all variable nodes, then device complexity is reduced, but decoding performance and error correction capability deteriorate
Solution Approach 1:
The patent segments variable nodes into multiple groups based on their graph-degree values and applies different decoding rules to each segment. This segmentation approach maintains relatively simple individual rules for each group while achieving superior overall error correction capability compared to uniform rules, as each segment receives treatment optimized for its specific characteristics.
Solution Approach 2:
The patent implements local quality by assigning decoding rules that are specifically tailored to the characteristics of each variable node group. Rather than applying a single uniform rule, the system provides locally optimized decoding rules for different graph-degree groups, thereby improving error correction capability without significantly increasing overall system complexity.
Data Source
AI summary
An error correction circuit includes a memory that stores at least one decoding parameter, a low density parity check (LDPC) decoder that includes a first variable node storing one bit of the data, receives the at least one decoding parameter from the memory, decides a degree of the first variable node based on the at least one decoding parameter, and decides a decoding rule necessary for decoding of the one bit based on the degree of the first variable node, and an adaptive decoding controller that outputs corrected data based on a decoding result of the LDPC decoder.