On-Demand FEC Decoding Using Syndrome Priority Selection
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Solution Overview
Problem
The high decoding complexity and power consumption in conventional forward error correction (FEC) decoding technologies, particularly in optical communications systems exceeding 100 Gbps, exceed product requirements and approach the Shannon limit, necessitating a more efficient decoding method.
Innovation Solution
A decoding method that prioritizes syndromes based on their non-zero values and decoding history, allowing on-demand decoding by selecting syndromes for decoding based on priority sorting, reducing unnecessary decoding operations and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional FEC decoding is performed on all codewords, then decoding completeness is ensured, but decoding complexity and power consumption increase significantly
Solution Approach 1:
The patent implements dynamic decoding by continuously monitoring syndrome values and adapting decoding actions in real-time. The system transitions from static decoding (processing all codewords uniformly) to dynamic decoding (selectively processing only non-zero syndromes based on current channel conditions), thereby reducing complexity while maintaining reliability
Solution Approach 2:
The patent extracts and processes only the essential subset of syndromes that actually require decoding (non-zero syndromes). By filtering out zero syndromes that indicate no errors, the system eliminates unnecessary decoding operations, reducing computational complexity and power consumption while maintaining complete error correction capability
2Reliability
If FEC decoding is performed on all codewords regardless of error status, then all potential errors are corrected, but power consumption increases
Solution Approach 1:
The patent applies partial action by performing decoding only on the necessary subset of codewords (those with non-zero syndromes) rather than all codewords. This selective approach consumes only the minimum required power to achieve complete error correction, eliminating waste while maintaining full reliability
Solution Approach 2:
The patent implements feedback through continuous syndrome monitoring that informs subsequent decoding decisions. The syndrome values provide real-time feedback about channel conditions and error status, enabling the system to adapt its decoding behavior dynamically and optimize power consumption based on actual needs
3Ease of manufacture
If static decoding solution is used, then implementation is simple, but decoding resource requirements are high
Solution Approach 1:
The patent introduces dynamic adaptation into the decoding process by monitoring syndrome patterns and adjusting decoding resource allocation in real-time. This allows the system to maintain simple implementation architecture while dynamically optimizing resource usage based on actual channel conditions and error rates
Data Source
AI summary
This application discloses decoding methods, apparatuses, and computer-readable storage media, which may be applied to a plurality of scenarios such as a metropolitan area network, a backbone network, and data center interconnection. An example method includes: obtaining syndromes corresponding to a plurality of codewords; grouping the syndromes into groups; and sorting priorities of each group of syndromes; and selecting, based on a priority sorting result of each group of syndromes, a syndrome for decoding.