Shared-Layer Data Decoding With Iterative Feedback Error Correction
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Solution Overview
Problem
Existing data processing systems face challenges in accurately decoding data due to errors introduced during transmission and storage, leading to corrupted information.
Innovation Solution
The implementation of a data processing system that includes both a data encoder circuit and a data decoder circuit, which encode and decode data sets using a multi-sector H-matrix with shared layer circuitry, generating codewords and applying feedback to correct errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data encoding and decoding methods are used, then the data processing is simple and fast, but errors are introduced during transmission and storage that corrupt the data
Solution Approach 1:
The patent implements an iterative feedback mechanism where the decoder generates hard decisions from soft values, re-encodes the decoded data, and compares the re-encoded output with the received codeword to generate syndromes. This feedback loop continues until convergence or maximum iterations are reached, enabling error correction while maintaining systematic structure.
Solution Approach 2:
The patent divides the decoding process into distinct stages: initial soft-to-hard decision conversion, first decoding pass, syndrome calculation, feedback generation, and iterative re-decoding. This segmentation allows each component to be optimized independently while working together to achieve reliable decoding.
2Reliability
If error correction mechanisms are implemented, then data reliability is improved, but the decoding process becomes more complex and computationally intensive
Solution Approach 1:
The patent employs iterative decoding that can terminate early when convergence is achieved, meaning the full computational sequence is not always executed. The decoder performs partial iterations based on syndrome results, stopping when errors are corrected or maximum iterations are reached, thus balancing error correction with processing speed.
Solution Approach 2:
The system performs preliminary soft-to-hard decision conversion and initial decoding before full iterative error correction is needed. This preliminary action provides a baseline decoded version that can be used if no errors are detected, avoiding unnecessary computational overhead in error-free scenarios.
3Measurement precision
If feedback-based re-decoding is applied, then decoding accuracy is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent implements periodic iterative decoding where the feedback-based re-decoding is applied in discrete cycles rather than continuously. Each iteration follows a fixed sequence of operations (decode, encode, compare, update) that repeats until convergence, providing structured periodic processing that balances precision with time management.
Solution Approach 2:
The decoding system uses its own output from previous iterations to generate feedback for subsequent iterations. The hard decisions from one decoding pass serve as input for the next pass, allowing the system to self-correct errors without requiring external intervention or additional computational resources beyond the iterative loop.
Data Source
AI summary
The present inventions are related to systems and methods for data processing, and more particularly to systems and methods for performing data decoding.