Phase-Noise Analysis of Optically Generated Millimeter-Wave Signals With External Optical Modulation Techniques
Bibliographic record
Abstract
In this paper, the phase-noise performance of optically generated electrical signals based on external optical modulation techniques is investigated theoretically and experimentally. Mathematical models are developed to represent perturbations on the transmitted optical signal caused by the phase fluctuations of the electrical drive signal applied to the external modulator and the optical carrier that feeds the external modulator. Closed-form expressions of the power spectral density (PSD) for the electrical signals, generated both locally and remotely, are derived. The calculated PSD of the locally generated electrical signal indicates that its phase noise is determined only by the phase noise of the electrical drive signal. The PSD of the remotely generated signal shows that its spectral quality is also affected by the chromatic dispersion of the fiber and the optical carrier linewidth. An experimental setup that can generate a millimeter-wave (mm-wave) signal, continuously tunable from 32 to 60 GHz using an electrical drive signal tunable from 8 to 15 GHz, is built. The spectra of the generated millimeter-wave signal are measured for both locally and remotely generated electrical signals, with optical carriers of different linewidths. The theoretical results agree with the experimental measurements
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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 | 0.000 | 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".