Performance evaluation of LDPC codes in the presence of ISI with application to 10Gbase-T ethernet
Bibliographic record
Abstract
Low density party check (LDPC) codes are among the most powerful error control codes known. They can produce error correcting performance close to the Shannon limit and can be decoded using iterative decoding algorithms with linear complexity. These key advantages have made LDPC codes a candidate coding scheme for various novel applications and standards, such as 10GBASE-T Ethernet, in which both coding performance and implementation complexity are important considerations. In this paper, we investigate the performance of a recent LDPC code candidate for 10GBASE-T Ethernet, as well as a standard LDPC code, over additive white Gaussian noise (AWGN) channels with inter-symbol interference (ISI). We show that in comparison with AWGN, ISI introduces less performance degradation at the same level of signal-to-noise-and-interference ratio (SNIR). Given this SNIR scenario, we give simulation evidence that the performance of LDPC codes over ISI channels is upper-bounded by their performance over a AWGN channel. These results provide insight into the performance evaluation of LDPC codes in practical systems. In particular, the results allow us to characterize the equivalent AWGN for given amounts of ISI
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".