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Record W7083287698 · doi:10.1109/jlt.2025.3614387

Broadening Gain Margin of an Optoelectronic Oscillator for Stable Single-Mode Oscillation

2025· article· en· W7083287698 on OpenAlexaff

Bibliographic record

VenueJournal of Lightwave Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicForeign Body Medical Cases
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBandwidth (computing)AmplifierSaturation (graph theory)Oscillation (cell signaling)Band-pass filterNonlinear systemdBmOptical amplifier

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.319
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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