Millimeter-wave carrier generation using an optical phase modulator and an optical notch filter
Bibliographic record
Abstract
The effective bandwidth of a wireless communication system is proportional to the carrier frequency, shifting the operating frequency of the wireless system from the crowded microwave L and S bands to the unregulated mm-wave band is a trend for future broadband wireless services. Intensity modulation and direct detection scheme (IM/DD) has been considered a simple method to impose a millimeter-wave signal onto optical fiber. However, for systems using IM/DD, the chromatic dispersion introduce significant power penalty, which limits the transmission distance. Remote heterodyne schemes have been proposed to solve the dispersion problem. Several approaches have been proposed to generate two phase-correlated optical wavelengths that are separated at a required millimeter-wave frequency. These approaches include single-side band with carrier modulation, optical carrier-suppressed modulation, optical offset injection locking and optical offset phase locking of two laser sources. All these methods provides phase-correlated optical wavelengths, but with complicated system configuration and high system cost. In this paper, we propose a simple method to generate phase-correlated wavelengths using a phase modulator and two fiber Bragg gratings. Two wavelengths are generated by modulating the phase modulator with an RF frequency. The required millimeter-wave signal is obtained by selecting two sidebands using the two narrowband fiber Bragg gratings. Theoretical analysis and experimental results will be reported in the paper.
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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 | 0.001 | 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".