Syndrome-Based Decoder for Low-Latency Symbol Error Correction
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Solution Overview
Problem
As communication speeds and data throughput increase, the number of error bits in information data also increases, leading to higher error correction latency in existing error correction decoders.
Innovation Solution
A decoder is designed with a syndrome generator that produces three syndromes based on a reception vector and a parity check matrix, and an error pattern classifier that utilizes mapping information to correct errors by determining symbol error locations and adjacent error vectors, reducing latency through efficient error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If communication speed and data throughput are increased, then data transmission efficiency is improved, but the number of error bits increases leading to higher error correction latency
Solution Approach 1:
The patent segments the error correction process into distinct stages: syndrome generation, error pattern classification, and error correction. By dividing the reception vector into multiple segments and processing them through different syndrome generators and classifiers in parallel, the system reduces overall correction latency while maintaining high throughput capability.
Solution Approach 2:
The patent implements preliminary classification of error patterns before full error correction. The error pattern classifier pre-identifies the type and location of errors using mapping information, allowing the subsequent correction stage to focus only on the identified error locations, thereby reducing the time required for complete error correction.
2Reliability
If the number of error bits increases, then error correction capability is improved, but error correction latency increases
Solution Approach 1:
The patent introduces an error pattern classifier as an intermediary component between syndrome generation and error correction. This classifier uses pre-stored mapping information to quickly identify error patterns, serving as a mediator that bridges the gap between raw syndrome data and correction operations, thereby reducing latency while maintaining correction capability.
Solution Approach 2:
The patent employs dynamic error correction strategies where the correction process adapts based on the classified error pattern. The system dynamically selects correction methods based on the type and distribution of errors identified, allowing efficient handling of varying error conditions without fixed-latency processing.
3Measurement precision
If mapping information between SASE and syndromes is utilized, then error pattern identification accuracy is improved, but decoder complexity increases
Solution Approach 1:
The patent pre-computes and stores mapping information between spotty and adjacent symbol error (SASE) patterns and their corresponding syndromes before decoding operations. This preliminary preparation allows the error pattern classifier to quickly lookup and identify error patterns during runtime without performing complex real-time calculations, thereby maintaining high identification accuracy while managing decoder complexity.
Solution Approach 2:
The patent uses pre-stored mapping tables that contain copied representations of error patterns and their syndrome relationships. Instead of reconstructing these relationships during decoding, the system copies and utilizes pre-established mappings, reducing computational complexity while preserving identification accuracy.
Data Source
AI summary
A decoder includes a syndrome generator and an error pattern classifier. The syndrome generator generates a first syndrome, a second syndrome, and a third syndrome based on a reception vector and a parity check matrix and outputs the first syndrome, the second syndrome, and the third syndrome, The error pattern classifier receives the first syndrome, obtains symbol error location information obtained based on mapping information between a spotty and adjacent symbol error (SASE) and the first syndrome, changes the first syndrome to a normalized syndrome based on a first received symbol, obtains an adjacent error vector based on mapping information between the normalized syndrome, the second syndrome, and the third syndrome and the SASE, and corrects an error of the reception vector based on the symbol error location information and the adjacent error vector.