Error Event Syndrome Selection for More Accurate Bit Decoding
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Solution Overview
Problem
In communication systems, identifying and correcting errors in decision bits is challenging due to noise and errors in received signals, especially in ISI channels and convolutional code encoders, where Viterbi detectors/decoders struggle to determine the correct trellis path, leading to inaccuracies in decoded data.
Innovation Solution
A system and method for error correction that involves computing error event syndromes and selecting the best and second-best error events based on their metrics, followed by LDPC decoding and post-processing to generate corrected decision bits, utilizing error event syndrome computation, per-syndrome selection, and log-likelihood ratio computation circuits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Viterbi detection/decoding is used to determine the most likely trellis path, then the decoding process can be completed, but errors may remain due to noise in received signals
Solution Approach 1:
The patent performs preliminary action by computing error event syndromes and identifying potential error events before final decoding. The system calculates syndromes for all possible error events, sorts them by likelihood metrics, and prepares correction candidates in advance, allowing the Viterbi decoder to work with pre-processed error information rather than raw noisy signals.
Solution Approach 2:
The patent implements feedback by using the syndrome computation results to guide error correction. The system continuously monitors error event metrics, identifies the most likely error patterns, and applies corrections based on this feedback loop. The syndrome values provide feedback about which error events are most probable, enabling iterative refinement of the decoded output.
2Measurement precision
If error correction is applied after Viterbi decoding, then decoding accuracy can be improved, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the error correction process into distinct modules: syndrome computation, error event identification, metric calculation, and correction application. Each module handles a specific aspect of error correction, making the overall complex system manageable through functional decomposition. The syndrome computation separates error detection from correction, while the metric calculation separates likelihood assessment from actual correction.
Solution Approach 2:
The patent uses syndrome computation as an intermediary between the noisy received signals and the final decoded output. The syndromes serve as intermediate representations that capture error information without requiring direct manipulation of the original noisy signals. This intermediary layer simplifies the correction process by working with compressed error indicators rather than full signal data.
3Reliability
If multiple error events are selected and processed, then error correction completeness improves, but processing time increases
Solution Approach 1:
The patent applies partial action by selecting only the most likely error events for correction rather than processing all possible error events. The system sorts error events by their likelihood metrics and processes only the top candidates (e.g., best and second-best error events). This partial processing approach achieves sufficient error correction without the computational burden of exhaustive analysis of all possible error patterns.
Data Source
AI summary
Circuitry and methods can be provided to correct errors in decision bits. A plurality of error event syndromes can be computed for a first plurality of error events. For each of a plurality of error event syndromes, two best error events can be selected. A cross-syndrome second best error event can be selected from among the first plurality of error events. A global second best error event can be selected from among the cross-syndrome second best error event and the second best per-syndrome error events. A second plurality of error events can be selected from among the global second best error event and the best per-syndrome error events. The second plurality of error events can be used for data post-processing.