Reconfigurable RF-Waveform Generation Based on Incoherent-Filter Design
Bibliographic record
Abstract
Radio-frequency (RF) waveform generators are key devices for a variety of applications, including radar, ultra-wideband communications, and electronic test measurements. Following advances in broadband coherent pulsed sources and pulse-shaping technologies, reconfigurable RF waveform generators operating at bandwidths$>$1 GHz have become a reality. In this work, we demonstrate reconfigurable RF waveform generation using broadband spectrally incoherent optical sources. This is achieved in two steps. First, we implement an RF incoherent filter. The energy spectrum of the optical source is conveniently apodized using a commercially available computer-controlled D-WDM channel selector with 100-GHz resolution. The channel controller provides high flexibility for shaping the optical source energy spectrum and, hence, high reconfigurability capabilities in terms of the RF filter. Second, we show that by applying a short baseband electrical waveform to the input of the RF filter, the output RF spectrum of the electrical signal is a mapped version of the designed RF filter transfer function. Specifically, we illustrate the capabilities of our technique by generating RF signals with$\sim$10 GHz bandwidth and tunable repetition rate. Finally, we discuss how this method can be scaled up to the millimeter-wave range with current technology.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".