Generation of Linearly Chirped Microwave Waveform With an Increased Time-Bandwidth Product Based on a Tunable Optoelectronic Oscillator and a Recirculating Phase Modulation Loop
Bibliographic record
Abstract
Photonic generation of a linearly chirped microwave waveform with an increased time-bandwidth product (TBWP) based on a frequency-tunable optoelectronic oscillator (OEO) and a recirculating phase modulation loop (RPML) is proposed and experimentally demonstrated, for the first time to the best of our knowledge. In the proposed system, a continuous-wave (CW) light wave is divided into two parts: one is sent to the tunable OEO to generate a frequency-tunable microwave signal and the other is intensity-modulated by a switching signal at an intensity modulator to form a chirp-free optical pulse, which are then sent to the RPML, in which the chirp-free pulse is phase modulated by a parabolic waveform to generate a linearly chirped optical waveform. The recirculation of the linearly chirped optical waveform inside the loop would lead to the waveform to experience multiple phase modulations, thus multiplying its chirp rate. By beating the chirped waveform and an optical sideband from the OEO at a high-speed photodetector (PD), a linearly chirped microwave waveform is obtained. The key significance of the approach is that the chirp rate is significantly increased, leading to a significantly increased TBWP. In addition, the approach allows the generation of a linearly chirped frequency-tunable microwave waveform without using a separate microwave source. The technique is experimentally verified. The generation of a linearly chirped microwave waveform with an increased TBWP by 16 times is demonstrated.
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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 | 0.001 |
| Open science | 0.001 | 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".