Transmission Performance of 448 Gb/s Single-Carrier and 1.2 Tb/s Three-Carrier Superchannel Using Dual-Polarization 16-QAM With Fixed LUT Based MAP Detection
Bibliographic record
Abstract
Transmission of single-carrier 448 Gb/s dual-polarization 16-ary quadrature-amplitude-modulation (DP 16QAM) electrical time-division multiplexed (ETDM) signal and a 1.206 Tb/s three-carrier superchannel ETDM signal using DP 16QAM is demonstrated by applying a fixed look-up table (LUT) based on maximum-a-posteriori (MAP) detection. The MAP detector is used to mitigate the effect of pattern dependent distortion in the transmitted signal arising from the high symbol rates. Different decision rules for the MAP detector are considered based on multiple observations of the same symbol as the detection window advances through the received symbol sequence. The back-to-back optical signal-to-noise ratios for a bit error ratio of 10-3are 26.9 and 26.4 dB for the 448 Gb/s signal (12% overhead with 7% for forward error correction (FEC) coding overhead) and 1.206 Tb/s superchannel signal (20% overhead with 15% for FEC coding overhead), respectively. Compared to conventional MAP detection, a seven-symbol minimum distance MAP detector reduces the required OSNR by about 0.8 and 1 dB at the FEC thresholds of 3.8 × 10-3and 1.9 × 10-2for the 448 Gb/s signal and 1.206 Tb/s superchannel signal, respectively. Transmission over 1200 and 1500 km of standard single-mode fiber with EDFA amplification is achieved for the 448 Gb/s signal and 1.206 Tb/s superchannel signal, respectively.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".