Fiber-optic Communications Under Direct Detection: Capacity Bounds & Tukey Signalling
Bibliographic record
Abstract
Because of its extremely simple implementation, intensity modulation with direct detection (IMDD) is widely employed in short-haul fiber-optic communication systems. However, since IMDD systems do not exploit phase information, they suffer from having a low spectral efficiency. Alternatively, being capable of measuring both the magnitude and the phase of the received complex-valued waveform, a coherent detector can provide high spectral efficiency, but at the price of having a complicated optical/optoelectronic receiver front-end. This high cost hinders the wide deployment of coherent detectors in short-haul applications. This thesis aims to introduce a new transceiver, compatible with short-haul fiber-optic communications, which has the simplicity of an IMDD receiver's optical front-end and the high spectral efficiency of a coherent detector. In this thesis, upper and lower bounds for the capacity of waveform channels under square-law detection of time-limited signals are derived. The upper bound is the capacity of the channel under phase-unlocked coherent detection. The lower bound is just one bit less, per dimension, than the upper bound. The lower bound suggests that the price in spectral efficiency of a simple direct-detection receiver is not as high as previously thought. To achieve the capacity lower bound, it is necessary both to modulate the magnitude and the phase of the transmitted waveform. However, it is not at all obvious how one may detect the magnitude and the phase of the transmitted complex-valued waveform only from intensity measurements of the received waveform. In this thesis, by introducing a new direct-detection-compatible signalling scheme, we address this question, as well. Extraction of phase information is made possible by the introduction of controlled inter-symbol interference, resulting in a scheme that achieves spectral efficiencies about one bit less, per second per hertz, than those of a coherent detector. The use of an integrate-and-dump detector in the proposed scheme makes precise waveform shaping unnecessary, thereby equipping the scheme with a high degree of robustness to nonlinear signal distortions introduced by practical modulators. Since maintaining linearity in the optical domain is expensive, this nonlinearity robustness is a main feature of the proposed scheme which makes it viable for short-haul applications.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".