Storage Channel Decoding for Short Error Event Correction
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Solution Overview
Problem
Conventional Error Correction Codes, such as LDPC codes, face challenges in effectively detecting and correcting short error events, particularly near-codeword and mis-correction errors, which can lead to inaccurate data retrieval in storage and communication systems.
Innovation Solution
The method involves partitioning a codeword into interleaved component codewords, decoding them along two dimensions, and using syndrome weights to detect and correct short error events by flipping bits in specific locations, thereby addressing near-codeword and mis-correction errors through iterative decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LDPC codes are used for error correction in storage applications, then error correction capability is improved, but susceptibility to short error events (near-codeword and mis-correction errors) increases
Solution Approach 1:
The codeword is divided into multiple component codewords arranged in an array structure with row and column dimensions. This segmentation allows the error correction system to process and detect errors in different dimensions independently, making it easier to identify and correct short error events that would be difficult to detect in a monolithic codeword structure.
Solution Approach 2:
The patent introduces a two-dimensional array structure for component codewords, enabling decoding along both row and column dimensions. This dimensional approach allows the system to detect short error events by comparing syndrome weights across different dimensions, effectively identifying near-codeword and mis-correction errors that single-dimensional approaches would miss.
2Productivity
If conventional decoding methods are used, then decoding speed is maintained, but accuracy in detecting and correcting short error events deteriorates
Solution Approach 1:
The patent performs preliminary decoding of component codewords along both row and column dimensions before final error correction. By pre-computing syndrome weights in multiple dimensions and identifying potential short error events early in the process, the system can apply targeted correction methods only where needed, maintaining overall decoding speed while improving accuracy.
Solution Approach 2:
The system uses syndrome weight calculations from both row and column decoding as feedback to identify short error events. The feedback mechanism compares expected syndromes with actual syndromes, allowing the decoder to detect near-codeword and mis-correction errors and apply appropriate correction strategies, thereby improving detection accuracy without significantly impacting decoding speed.
Data Source
AI summary
A method for decoding a codeword includes partitioning the codeword into a plurality of component codewords where each of the component codewords comprising a respective plurality of bits. The respective plurality of bits in each of the plurality of component codewords are interleaved. Each of the plurality of interleaved component codewords are decoded along two dimensions to produce (i) a set of first decoding results and (ii) a set of second decoding results. A short error event is then detected and corrected based on (i) the set of first decoding results and (ii) the set of second decoding results.