A Study of Discrete Multitone Modulation for Wireline Links Beyond 100 Gb/s
Bibliographic record
Abstract
To overcome the severe losses beyond 28 GHz in low-cost electrical channels, 4-level pulse-amplitude modulation (4-PAM) wireline links targeting 112 Gb/s incorporate resourceor power-intensive equalization schemes such as decision-feedback equalizers (DFE) with many taps. Alleviating the timing constraints that cause DFEs to balloon in size and power, discrete multitone (DMT) modulation involves independent sub-channels that can be equalized in the frequency domain without feedback. DMT allows flexibility in assigning bits to each sub-channel, thereby potentially avoiding lossy parts of the frequency spectrum. This article presents behavioural modeling results and an experimental setup used to study DMT transceivers. Our simulations show 200 Gb/s operation at a bit error rate (BER) of less than 10-5over an IEEE P802.3ck channel with 21 dB of loss at 50 GHz and assuming 150 fsrms of jitter, 1.26 mVrms of noise at the receiver's input, and 7-bit 80 GS/s data converters. An updated bit-loading algorithm led to BER values 1-2 orders of magnitude below our previous results. The estimated power and area of the associated digital signal processing are comparable to those of DFE. We also present an experimental DMT setup achieving 61.6 Gb/s at a BER of 8.6 × 10-4over a physical channel with severe notches that is very challenging for 4-PAM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".