Photonic Generation of a Phase-Coded Chirp Microwave Waveform With Increased TBWP
Bibliographic record
Abstract
Photonic generation of a phase-coded chirped microwave waveform with an increased time bandwidth product (TBWP) using a frequency-tunable optoelectronic oscillator (OEO) is proposed and experimentally demonstrated. The frequency-tunable OEO is implemented using a tunable laser source (TLS), a phase modulator (PM), a phase-shifted fiber Bragg grating, and a photodetector (PD), with the frequency tuning realized by tuning the wavelength of the TLS. A frequency-tunable optical sideband with a frequency that is equal to that of the optical carrier plus the OEO oscillation frequency is generated by the OEO, which is then orthogonally polarization multiplexed with the optical carrier from the TLS at a polarization beam combiner, and applied to a polarization modulator, to which a binary phase-coded parabolic electrical signal is applied. By beating the two orthogonally polarized optical signals at a PD, a phase-coded chirped microwave waveform is generated. The TBWP is significantly increased due to the increase of the temporal duration of the microwave waveform. The proposed approach is experimentally demonstrated. Two phase-coded chirped microwave waveforms with TBWPs of 58.5 and 80 000 using two phase coding signals corresponding to a 13 Barker code and a 20480-bit pseudorandom sequence are generated.
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".