Flash Memory Soft Decoding Using Quantized Reliability Intervals
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Solution Overview
Problem
Existing error-correcting codes struggle to efficiently compute the reliability of decoding in complex systems, particularly in flash memory and wireless communication systems, where the system model is too complicated for reliable decoding computations.
Innovation Solution
A method of soft decoding received signals by defining quantization intervals, determining the number of bits and unsatisfied constraints, calculating the overall bit error rate, and using log likelihood ratios for soft decoding, which simplifies the computation of decoding reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system model is used to compute decoding reliability, then the reliability of decoding indication is improved, but the computational complexity becomes too high to be performed efficiently
Solution Approach 1:
The patent segments the continuous signal value range into discrete quantization intervals. Instead of computing reliability for every possible signal value, the system divides the range into intervals and computes representative reliability values for each interval. This segmentation reduces the computational burden while maintaining adequate decoding reliability indication.
Solution Approach 2:
The patent transforms the complex system model computation into a simplified parameter-based approach. By using quantization intervals and pre-computed or pre-stored reliability parameters associated with each interval, the system avoids direct computation from the full system model during decoding operations, thereby reducing computational complexity while preserving reliability information.
2Device complexity
If quantization intervals are defined and log likelihood ratios are computed, then computational complexity is reduced, but measurement precision of decoding reliability may be degraded
Solution Approach 1:
The patent applies local quality by associating different reliability characteristics with different quantization intervals. Each interval receives a tailored log likelihood ratio computation based on its specific signal value range and characteristics. This allows the system to maintain high precision where needed while using simplified computations in other regions, optimizing the balance between complexity and precision.
Solution Approach 2:
The patent computes log likelihood ratios for all quantization intervals, providing more reliability information than the minimum required for basic decoding. This excessive computation of reliability metrics across all intervals ensures that sufficient precision is maintained even after quantization, while still being more efficient than full system model computation.
Data Source
AI summary
A method of soft decoding received signals. The method comprises defining quantisation intervals for a signal value range, determining a number of bits detected in each quantisation interval, a number of bits in each quantisation interval that are connected to unsatisfied constraints and a probability that the error correction code is unsatisfied, determining an overall bit error rate based on the probability that the error correction code is unsatisfied, determining a log likelihood ratio for each quantisation interval based on the overall bit error rate, the number of bits detected in each quantisation interval and the number of bits in each quantisation interval that are connected to unsatisfied constraints and performing soft decoding using the log likelihood ratios.