Gain Optimization by Modulator-Bias Control in Radio-Over-Fiber Links
Bibliographic record
Abstract
The authors propose a method to optimize the RF gain in narrowband radio-over-fiber links employing a Mach-Zehnder modulator followed by an erbium-doped fiber amplifier (EDFA) for amplification. Optimization is achieved by control of the modulator bias in order to improve the signal optical-modulation depth (OMD). Thus, for a given modulation amplitude, the optical signal has a reduced mean optical power and can access the small signal gain of the EDFA. This unsaturated gain is higher than the saturated one, thereby significantly increasing the RF gain of the link. Simultaneous optimization of OMD is also desirable to reduce detector saturation and fiber-induced nonlinear effects. They derive an analytical expression to describe optimum operating conditions for the modulator bias and validate their results through numerical simulation and experimental work. The proposed optimum modulator operating point is experimentally proven to be applicable to multicarrier signals like those used in 802.11a/g protocols
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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 | 0.001 |
| 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".