LDPC Error Correction Circuit With Degree-Based Decoding Rules
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Solution Overview
Problem
Low density parity check (LDPC) error correction methods face high complexity and an increased error floor due to approximation, which limits their effectiveness in improving signal-to-noise ratio (SNR) versus bit-error rate (BER) performance.
Innovation Solution
An error correction circuit and method that includes a memory for storing decoding parameters and a processing circuit to determine graph-degrees of variable nodes, adjust decoding parameters based on error rates, and output corrected data by iteratively performing LDPC decoding with adaptive decoding rules, thereby reducing the error floor and improving BER with increasing SNR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LDPC method is used for error correction, then error correction capability is improved, but calculation complexity increases
Solution Approach 1:
The patent segments the variable nodes into different groups based on their graph-degree values (e.g., degree-3, degree-4, degree-5 variable nodes). Each group is decoded using tailored decoding rules optimized for its specific degree characteristics. This segmentation allows the system to apply simpler, more efficient decoding operations to each subgroup rather than using a single complex decoding algorithm for all nodes, thereby reducing overall calculation complexity while maintaining error correction capability.
Solution Approach 2:
The patent applies different decoding rules to different variable nodes based on their local graph-degree properties. Specifically, degree-3 variable nodes use one decoding rule, degree-4 variable nodes use another rule, and degree-5 variable nodes use a third rule. This local quality approach ensures that each node is processed with the most appropriate algorithm for its characteristics, optimizing the balance between correction effectiveness and computational efficiency for each local region of the code graph.
2Device complexity
If approximation is applied to reduce LDPC complexity, then calculation complexity decreases, but error floor increases
Solution Approach 1:
The patent changes the decoding parameters (decoding rules) based on the graph-degree parameter of variable nodes. Instead of using a fixed approximation for all nodes, the system selects from multiple decoding rules depending on the degree of each variable node. This parameter adaptation allows the system to maintain higher accuracy for nodes where it is critical (reducing error floor) while still applying simplified rules where appropriate (controlling complexity).
3Device complexity
If uniform decoding rule is applied to all variable nodes, then device complexity decreases, but error correction performance deteriorates
Solution Approach 1:
The patent applies different decoding rules to different variable nodes based on their local graph-degree properties. Specifically, degree-3 variable nodes use one decoding rule, degree-4 variable nodes use another rule, and degree-5 variable nodes use a third rule. This local quality approach ensures that each node is processed with the most appropriate algorithm for its characteristics, optimizing the balance between correction effectiveness and computational efficiency for each local region of the code graph.
Solution Approach 2:
The patent segments the variable nodes into different groups based on their graph-degree values (e.g., degree-3, degree-4, degree-5 variable nodes). Each group is decoded using tailored decoding rules optimized for its specific degree characteristics. This segmentation allows the system to apply simpler, more efficient decoding operations to each subgroup rather than using a single complex decoding algorithm for all nodes, thereby reducing overall calculation complexity while maintaining error correction capability.
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.