Broadening Gain Margin of an Optoelectronic Oscillator for Stable Single-Mode Oscillation
Bibliographic record
Abstract
The gain margin of an optoelectronic oscillator (OEO) is the range of gain (RoG) over which stable single-mode oscillation is maintained without being switched to oscillations in multi-mode. A large RoG indicates a stable single-mode oscillation against gain variations due to environmental perturbations. For an OEO incorporating an electrical bandpass filter (EBPF) and an electrical amplifier (EA), we propose to increase the RoG by reducing the bandwidth of the EBPF and by reducing the saturation output power of the EA. The proposed scheme is studied by numerical simulations and is verified by an experiment. The simulation results based on the extended microwave-photonic iterative nonlinear gain (eMING) model show that reducing the bandwidth of the EBPF from 1 GHz to 20 MHz, the RoG is increased by 5.5 dB. Additionally, lowering the saturation output power of the EA from 16 dBm to 3 dBm, the RoG is increased by 9 dB. The simulation results are then verified by an experiment. Experimental results demonstrate a 3.6 dB improvement in RoG by reducing the bandwidth of the EBPF from 1 GHz to 20 MHz. Similarly, the RoG shows a 2.6 dB improvement by lowering the saturation output power of the EA from 16 dBm to 3 dBm. This work provides an effective solution to maximize the stable single-mode operation range of an OEO without complicating the system architecture.
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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 | 0.000 |
| 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".