Single-Lane 145 Gbit/s IM/DD Transmission With Faster-Than-Nyquist PAM4 Signaling
Bibliographic record
Abstract
The performance of faster-than-Nyquist intensity modulation and direct detection (IM/DD) systems can be severely degraded by the inter-symbol interference (ISI) due to the component bandwidth limitation and the faster-than-Nyquist filtering. In addition, nonlinearities caused mainly by optical modulation and square-law detection can further deteriorate signal quality. Here, we propose to incorporate transmitter-side Tomlinson-Harashima precoding to mitigate the ISI and a Volterra nonlinear equalizer (VNLE) to mitigate the nonlinear distortions for PAM4 faster-than-Nyquist systems. We experimentally demonstrate a single wavelength 145 Gb/s PAM4 IM/DD transmission with bandwidth limited commercial components and sub-Nyquist sampling at 70 GSa/s. The optimal VNLE filter length is also investigated to balance the implementation complexity and system performance. In addition, compared with the scheme using transmitter-side duobinary precoding, receiver-side VNLE and maximum likelihood sequence estimation, both performance improvement and implementation complexity reduction are achieved.
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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 | 0.001 | 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".