LDPC Bit-Flipping Decoding Using Historical Flipping Energy
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Solution Overview
Problem
Existing Bit-Flipping (BF) decoding algorithms for Low-Density Parity Check (LDPC) codes suffer from lower performance and higher iteration counts, limiting their efficiency in error correction, particularly in noisy channels.
Innovation Solution
Incorporating historical decoding information to compute flipping energy for variable nodes, using a flipping energy threshold to determine node updates, and optimizing parameters to reduce iteration counts and improve decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional Bit-Flipping decoding algorithm is used, then decoding complexity is reduced, but decoding performance deteriorates and iteration count increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing historical decoding information (such as previous iteration results, flipping patterns, and convergence trends) before the actual decoding process. This historical data is then reused during decoding iterations to guide bit-flipping decisions, allowing the algorithm to achieve better performance without increasing computational complexity during the decoding phase.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring decoding iteration results and using this information to adjust subsequent flipping decisions. The historical decoding information serves as feedback that informs the flipping energy calculation, enabling the algorithm to learn from past iterations and improve convergence behavior without adding significant complexity.
2Device complexity
If traditional Bit-Flipping decoding algorithm is used, then decoding complexity is reduced, but convergence speed deteriorates due to higher iteration count
Solution Approach 1:
By pre-computing and storing historical decoding patterns and convergence information before the actual decoding process, the algorithm can quickly reference these pre-prepared data structures during iterations. This eliminates the need for complex real-time calculations while accelerating convergence through informed flipping decisions based on historical trends.
Solution Approach 2:
The patent introduces dynamic adaptation by allowing the decoding algorithm to adjust its flipping strategy based on historical information that captures convergence behavior. The flipping energy calculation dynamically incorporates lessons learned from previous iterations, enabling the algorithm to adapt its convergence path without increasing structural complexity.
3Reliability
If historical decoding information is incorporated, then decoding performance is improved and convergence speed is increased, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential historical decoding information that is most relevant to improving flipping decisions, such as previous iteration outcomes and flipping patterns. By selectively extracting and storing only the critical historical data rather than all possible information, the algorithm achieves performance improvement while minimizing the increase in computational complexity.
Solution Approach 2:
The patent changes the parameters used in flipping energy calculation by incorporating historical decoding information as additional factors. This modifies the flipping decision criteria to include learned patterns from past iterations, improving performance while the parameter changes are designed to maintain computational efficiency through straightforward extensions of existing calculations.
Data Source
AI summary
Disclosed are a controller, a system and a method for decoding LDPC codewords based on historical decoding information. In a decoding iteration, the value of flipping energy of a variable node in a codeword is computed based on information representing decoding of the codeword before the decoding iteration. A comparison result is obtained by comparing the value of flipping energy to a flipping energy threshold. And the variable node is flipped in response to the comparison result satisfying a criterion. A controller with a process configured to implement the BF decoding process, as well as a system with the controller.