LDPC Decoder Resource Allocation for Irregular Regular-Code Decoding
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Solution Overview
Problem
Existing LDPC decoders face inefficiencies in error correction due to the pollution of lower quality data affecting higher quality data, leading to slowed convergence and reduced decoding performance.
Innovation Solution
Implementing irregular decoding of regular codes by sorting data by quality and allocating more decoding resources to higher quality data in early iterations, freezing updates for lower quality data, and reassessing in later iterations to assist convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regular decoding is used where all data is processed uniformly in each iteration, then hardware complexity is reduced, but lower quality data pollutes higher quality data causing slower convergence
Solution Approach 1:
The patent applies local quality by differentiating the processing of data based on its quality metrics. High-quality data (with confidence metric above threshold) receives full processing attention in each iteration, while low-quality data (with confidence metric below threshold) is frozen and excluded from processing. This selective processing prevents low-quality data from polluting high-quality data, improving convergence speed and decoding performance without requiring complex hardware modifications.
2Reliability
If more decoding iterations are performed to improve convergence, then error correction performance improves, but processing time increases
Solution Approach 1:
The patent implements preliminary action by performing a confidence metric check on data before each processing iteration. This preliminary assessment identifies which data elements are ready for processing (high confidence) and which should be frozen (low confidence). By pre-screening data quality, the system avoids wasting processing iterations on low-quality data that cannot contribute meaningfully to convergence, thereby reducing total processing time while maintaining error correction performance.
Solution Approach 2:
The patent applies dynamics by making the processing status of data elements dynamic rather than static. Data elements transition between two states: processed (when confidence metric exceeds threshold) and frozen (when confidence metric is below threshold). This dynamic state change allows the system to adaptively adjust processing focus based on data quality, accelerating convergence for high-quality data while preventing iterative refinement of low-quality data that would only increase processing time without improving results.
Data Source
AI summary
The present inventions are related to systems and methods for irregular decoding of regular codes in an LDPC decoder, and in particular to allocating decoding resources based in part on data quality.