Bibliographic record
Abstract
In this thesis we present a set of systematic methodologies to statistically analyze and optimize high-speed wireline transceivers subject to DFE error propagation, with particular focus on post-FEC BER using non-binary linear block codes. We propose a statistical model that can accurately simulate post-FEC BERs in less than a few seconds using the standard Reed–Solomon KP4 and KR4 FEC codes. Several techniques including state aggregation, time aggregation, state reduction, and dynamic programming are introduced to make the time complexity to compute post-FEC BER below 10^-15 reasonable. The proposed statistical model can consider bit multiplexing, 1/(1+D) precoding, FEC interleaving and key noise sources in wireline transceivers. We present two important contributions to the IEEE and OIF wireline standards on modeling concatenated FECs and error propagation factors. We apply our proposed statistical model to explore global transceiver optimization based on a genetic algorithm using various cost functions that minimizes post-FEC BER. We showed that, in general, links attain their minimum post-FEC BER with equalizer coefficients very different from those that minimize pre-FEC BER. We analytically and experimentally verify that using a genetic algorithm can successfully find equalizer coefficients that lead to the globally optimal BER. Our proposed statistical method provides a set of tools to assist in making architectural choices for wireline transceivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".