Phase noise analysis of the optically generated and distributed millimeter wave signal using an external optical modulator
Bibliographic record
Abstract
Distribution of millimeter-wave signals over optical fiber has been considered a promising technology for future broadband wireless access networks, thanks to the low loss and broad bandwidth of optical fibers operating at the 1550 nm window. Different schemes have been proposed to distribute millimeter-wave signals using optical fiber, which include intensity modulation and direct detection (IM/DD) scheme and remote heterodyne (RHD) scheme. In a millimeter-wave-over-fiber system using IM/DD scheme, two sidebands located at the two sides of the optical carrier are generated. For frequencies higher than 20 GHz, the chromatic dispersion becomes a serious problem which leads to high power penalty. The dispersion problem can be solved if RHD scheme is used. In an RHD scheme, two wavelengths that are phase correlated are generated using single-side band with carrier modulation, optical carrier-suppressed modulation, optical offset injection locking or optical offset phase locking of two laser sources. Ideally, the laser sources are considered to have very narrow linewidth, which will not introduce phase noise at the remote side when beating the two wavelengths. However, in real applications laser diodes usually have a finite linewidth, which leads to the phase de-correlation in the fiber links; phase noise is then generated at the remote end. In this paper, we will analyze the effects of the finite linewidth of optical sources on the performance of millimeter-wave over fiber systems. Simulation and experimental results will be provided.
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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) | 0.001 | 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".