Multilevel amplitude shift keying in dispersion uncompensated optical systems
Bibliographic record
Abstract
The authors model and characterise the performance of 10 Gbit/s 4-ary amplitude shift keying (ASK) systems in dispersive environments at 1550 nm. Both non-return-to-zero (NRZ) and return-to-zero (RZ) formats are examined, with comparisons to on-off keying (OOK) in each case. Both single amplified links and cascaded fibre-amplifier links are modelled. While 4-ary ASK systems suffer a back-to-back sensitivity penalty of up to 7 dB with respect to OOK, they offer a significantly reduced dispersion sensitivity, particularly for RZ formats. This suggests advantages for M-ary coding in future systems employing optical time division multiplexing. Optimal level spacing for ASK is analysed and approximations for different noise regimes are shown to fit well to detailed calculations. Sensitivity to extinction ratio is examined; RZ systems are shown to have higher sensitivity than NRZ for both 4-ary and OOK. Finally, the authors show that frequency drift, or equivalently, a de-tuning in the centre frequency of the optical filter, is more severe for OOK than for 4-ary ASK, especially in the case of non-fully dispersion uncompensated systems.
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 | 0.000 | 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 | 0.001 | 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".