Syndrome Calculation Segmentation for Faster Error Correction
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Solution Overview
Problem
Existing error correction methods are inefficient in detecting and correcting errors, particularly due to the computational intensity and resource requirements of syndrome calculations.
Innovation Solution
A method for error correction that involves a syndrome calculation using a matrix M, transformed by a linear mapping, to determine an error vector efficiently, thereby correcting erroneous data words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional syndrome calculation methods are used for error correction, then error detection and correction can be achieved, but the computational complexity and resource requirements are high
Solution Approach 1:
The syndrome calculation is segmented into two distinct phases: a first syndrome calculation phase that processes all received code words (including error-free ones) to generate preliminary syndrome values, and a second phase that only processes code words with non-zero syndromes to determine final error correction data. This segmentation eliminates unnecessary computations for error-free code words, reducing overall computational complexity while maintaining error correction reliability.
Solution Approach 2:
The patent performs preliminary syndrome calculations for all received code words before determining which ones actually require error correction. By calculating syndromes in advance and identifying only those code words with non-zero syndromes, the system prepares error correction data selectively, avoiding the computational waste of processing error-free code words through full error correction algorithms.
2Reliability
If traditional syndrome calculation methods are used for error correction, then error detection and correction can be achieved, but the resource consumption is high
Solution Approach 1:
The computation process is divided into segments where resource-intensive error correction operations are performed only on code words with non-zero syndromes. The first segment calculates syndromes for all code words using minimal resources, and the second segment performs full error correction only when necessary, significantly reducing overall resource consumption including energy, processing power, and memory usage.
Solution Approach 2:
Instead of performing complete error correction processing on every received code word, the patent applies partial processing by first computing only the syndrome (a minimal necessary operation) for all code words, and then applying the full correction algorithm only to the subset of code words that actually contain errors. This partial action approach reduces resource consumption by avoiding excessive processing of error-free data.
Data Source
AI summary
Error correction is proposed, wherein, on the basis of a data word, a syndrome calculation is carried out with a matrix M on the basis of a matrix H of a code, and, if the result of the syndrome calculation reveals that the data word is erroneous, the result of the syndrome calculation is transformed by means of a linear mapping. Next, an error vector is determined on the basis of the result of the linear mapping by means of an efficient error correction algorithm and the erroneous data word is corrected on the basis of the error vector.