Decoder Sector Quality Estimation Using Probabilistic Symbol Errors
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Solution Overview
Problem
The number of unsatisfied checks (USCs) does not accurately reflect the symbol error rate in decoding processes, particularly in low error rate regimes, leading to poor correlation with actual symbol errors.
Innovation Solution
A probabilistic method for symbol error rate estimation in decoders, involving the calculation of probabilities for check nodes being unsatisfied, identification of most ambiguous variable nodes, and log-likelihood ratio (LLR) calculations to select and analyze check nodes with higher probabilities of being unsatisfied, allowing for a more precise estimation of symbol errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of unsatisfied checks (USCs) is used to estimate symbol error rate, then the estimation process is simple, but the measurement precision is poor especially in low error rate regimes
Solution Approach 1:
The patent segments the decoder analysis by identifying and focusing on the most ambiguous variable nodes connected to each check node. Instead of treating all check nodes uniformly, it divides them into segments based on their ambiguity levels and selects only the most critical ones for detailed analysis, thereby improving measurement precision without proportionally increasing overall complexity
Solution Approach 2:
The patent changes the parameter used for estimation from the raw number of unsatisfied checks to a probability-based metric derived from log-likelihood ratios (LLR). By transforming the estimation parameter from a simple count to a probabilistic measure that accounts for node ambiguity, the patent achieves higher accuracy in symbol error rate estimation while maintaining computational feasibility
2Measurement precision
If probability calculations and LLR computations are performed for all check nodes, then the symbol error rate estimation accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the most relevant information from the decoding process by identifying and focusing on the most ambiguous variable nodes. Instead of processing all check nodes equally, it extracts and analyzes only those check nodes with the highest ambiguity levels, thereby achieving high estimation accuracy with reduced computational effort and processing time
Solution Approach 2:
The patent applies partial action by performing detailed probability and LLR calculations only for a selected subset of check nodes (k+1 most ambiguous ones) rather than all check nodes. This partial analysis approach provides sufficient accuracy for symbol error rate estimation while significantly reducing the computational burden and processing time compared to exhaustive analysis
Data Source
AI summary
A probabilistic approach of symbol error estimation is disclosed. The probabilistic approach of symbol error estimation reflects the number of symbol errors more precisely than the number of unsatisfied checks. The more precise quality metric calculated in accordance with the present disclosure allows a codec system to achieve a better overall performance. In addition, many other features that previously depend on the number of unsatisfied checks as the sector quality metric may also benefit by adopting the more precise quality metric.