Practical Considerations in Direct Detection Under Tukey Signalling
Bibliographic record
Abstract
The deliberate introduction of controlled intersymbol interference (ISI) in Tukey signalling enables the recovery of signal amplitude and (in part) signal phase under direct detection, giving rise to significant data rate improvements compared to intensity modulation with direct detection (IMDD). The use of an integrate-and-dump detector makes precise waveform shaping unnecessary, thereby equipping the scheme with a high degree of robustness to nonlinear signal distortions introduced by practical modulators. Signal sequences drawn from star quadrature amplitude modulation (SQAM) formats admit an efficient trellis description that facilitates codebook design and low-complexity near maximum-likelihood sequence detection in the presence of both shot noise and thermal noise. Under the practical (though suboptimal) allocation of a 50% duty cycle between ISI-free and ISI-present signalling segments, at a symbol rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50\,$</tex-math></inline-formula> Gbaud and a launch power of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-10\,$</tex-math></inline-formula> dBm the Tukey scheme has a maximum theoretically achievable throughput of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$200\,$</tex-math></inline-formula> Gb/s with an (8,4)-SQAM constellation, while an IMDD scheme achieves about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$145\,$</tex-math></inline-formula> Gb/s using PAM-8. Note that the two mentioned constellations have the same number of magnitude levels and the difference in throughput is resulting from exploiting phase information under using a complex-valued signal constellation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".