Microwave Photonic Link With Improved Dynamic Range for Long-Haul Multi-Octave Applications
Bibliographic record
Abstract
A technique to suppress both the second and third order nonlinear distortions in a long-haul microwave photonic link (MPL) is presented. The MPL consists of a dual-polarization dual-parallel Mach-Zehnder modulator (DP-DPMZM), a polarizer, a length of fiber, an optical bandpass filter (OBPF), and a balanced photodetector (BPD). The DP-DPMZM has two sub-DPMZMs. For a two-tone signal atf1andf2, the second order harmonic distortions (SHD) and the second order intermodulation distortions (IMD2) atf2+f1are suppressed by biasing one sub-DPMZM to operate with single-sideband suppressed carrier (SSB-SC) modulation. The IMD2 atf2–f1is suppressed by balanced detection at the BPD. The third order intermodulation distortions (IMD3) are suppressed by adjusting the state of polarization of the light wave into the polarizer. The spurious free dynamic range (SFDR) in a multi-octave band is free from fiber dispersion effect due to the bias setting of the DP-DPMZM. As a result, a high multi-octave SFDR can be obtained irrespective of the transmission distance. The proposed MPL is analyzed theoretically and is verified experimentally. The measured multi-octave SFDR is 95.9 dB·Hz1/2and 92.1 dB·Hz1/2when a long fiber link at 10 km and 20 km is used for signal transmission, respectively, which is 16.1 dB and 17.3 dB higher compared to a conventional Mach-Zehnder modulator (MZM) based MPL operating under the same condition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".