Generation of Stepped Frequency Microwave Waveforms in the Temporal-Frequency Synthetic Dimension With Analog Predistortion
Bibliographic record
Abstract
A novel approach to the generation of stepped frequency microwave waveforms (SFMW) in the temporal-frequency synthetic dimension with analog predistortion is proposed and experimentally demonstrated. The system consists of two coupled fiber-optic loops with an acoustic-optic modulator (AOM) incorporated in each of the two loops. When a light wave is passing through an AOM, the amplitude of the light wave is altered and its frequency is shifted, depending on the applied driving signal. The dynamic amplitude modulation and frequency shift to the optical pulses recirculating in the system implemented by the AOMs establish a temporal-frequency synthetic dimension. For SFMW generation, a seed optical pulse with a fixed carrier frequency is injected into the system. The frequency and amplitude of the optical pulse after each recirculation are determined by the frequencies and strengths of the driving signals applied to the AOMs. By applying two pre-designed driving signals to the AOMs, a stepped frequency pulse burst with the designated magnitude profile is generated after the seed pulse recirculates in the two loops for multiple round trips. By beating the pulse burst with the same optical carrier, an SFMW can be generated. The proposed approach is evaluated experimentally. Two SFMWs with predesigned spectral magnitudes having stepped frequencies from 5.44 to 7.24 GHz and 7.12 to 8.92 GHz are generated. The control of the spectral magnitude can be employed for spectrum distortion compensation. For a microwave transmitter frontend with an uneven magnitude response of up to 3.24 dB, transmitted microwave waveforms with flattened spectra having unevenness of less than 1.11 dB are achieved.
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 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".