Frequency upconversion of multiple RF signals using optical carrier suppression for radio over fiber downlinks
Bibliographic record
Abstract
We propose and analyze a technique of an optical carrier transmitting two RF signals using optical carrier suppression. A single optical Mach-Zehnder modulator is used for both optical carrier suppression and signal modulation, and optical carrier suppression modulation is also used for frequency conversion of RF signals. This work shows that in contrary to the case of an optical carrier transmitting a single RF signal with optical carrier suppression where stronger optical carrier suppression improves the upconverted RF signal, weaker optical carrier suppression is preferred for an optical carrier transmitting two RF signals due to nonlinear distortion because the nonlinear distortion is reduced by using weaker optical carrier suppression. We find that the usable range of optical carrier suppression ratio is from 10 to 18 dB for RF signal upconverted to 20 GHz and beyond, and the best optical carrier suppression ratio is around 10 dB. We verify the concept and analysis with experiment. In experiment, we used two RFs at 6 and 18 GHz transmitting two 750 Mb/s signals. The experiment for the first time demonstrated that an optical carrier can transmit two RF signals using optical carrier suppression and showed that upconverted RF signals are degraded by nonlinear distortion, particularly for upconverted RF signal at 12 GHz, i.e. the RF signal at the lower frequency.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".