Segmented Soft-Metric FEC for Lower-Complexity Error Correction
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Solution Overview
Problem
Existing forward error correction (FEC) devices in high-data-rate networks, such as optical networks, incur high costs and complexity due to the large quantity of bits processed and stored during soft iterative error correction, limiting their efficiency and error correction capabilities.
Innovation Solution
A system and method that process encoded words on a segment-by-segment basis, using extrinsic information to update reliability levels and identify least reliable positions, generating candidate words, and performing FEC on a per segment basis to reduce the number of bits used and stored, allowing for a greater range of reliability values and fractional bits, thereby enhancing error correction capacity and reducing processing time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soft iterative error correction is performed on entire encoded words, then error correction capability is improved, but hardware cost and complexity increase due to processing and storing large quantities of bits
Solution Approach 1:
The encoded word is divided into multiple segments, and error correction is performed iteratively on each segment separately rather than on the entire word. This segmentation reduces the number of bits that need to be processed and stored simultaneously, thereby reducing hardware complexity and cost while maintaining error correction capability through multiple iterative passes.
Solution Approach 2:
Instead of performing complete error correction on the entire encoded word in one pass, the method performs partial correction on individual segments across multiple iterations. This partial action approach processes fewer bits at each step, reducing immediate hardware requirements while achieving comprehensive error correction through repeated partial operations.
2Reliability
If soft iterative error correction is performed on entire encoded words, then error correction capability is improved, but processing time increases due to large quantity of bits to process
Solution Approach 1:
By dividing the encoded word into smaller segments and processing each segment separately in iterative passes, the method reduces the processing time per iteration. Although multiple iterations are performed, each iteration processes fewer bits, making the overall processing more efficient compared to processing the entire word in a single pass.
Solution Approach 2:
The method performs partial error correction on segments rather than complete correction on the entire word in one operation. This partial action reduces the computational burden per step, decreasing processing time while achieving full error correction through multiple lighter operations.
3Reliability
If a greater range of reliability values and fractional bits are used, then error correction capacity is enhanced, but the quantity of bits to be processed and stored increases
Solution Approach 1:
The method divides the encoded word into segments and processes reliability information for each segment separately. This segmentation allows the system to use greater ranges of reliability values and fractional bits within each segment without proportionally increasing the total bits to be processed and stored across the entire word, as only one segment needs to be held in memory at a time during each iteration.
Data Source
AI summary
A system is to receive a word on which to perform error correction; obtain segments, from the word, each segment including a respective subset of samples; update, on a per segment basis, the word based on extrinsic information associated with a previous word; identify sets of least reliable positions (LRPs) associated with the segments; create a subset of LRPs based on a subset of samples within the sets of LRPs; generate candidate words based on the subset of LRPs; identify errors within the word or the candidate words; update, using the extrinsic information, a segment of the word that includes an error; determine distances between the candidate words and the updated word that includes the updated segment; identify best words associated with shortest distances; and perform error correction, on a next word, using other extrinsic information that is based on the best words.