Impact of nonlinear distortion in radio over fiber systems with single-sideband and tandem single-sideband subcarrier modulations
Bibliographic record
Abstract
In radio over fiber (RoF) systems, two subcarrier modulations (SCMs), i.e., single-sideband (SSB) and tandem single-sideband (TSSB), have been widely used. Both SSB and TSSB SCMs can be obtained using optical Mach-Zehnder modulators. In this paper, the authors theoretically investigate the impact of harmonic distortion and intermodulation distortion in RoF systems for one wavelength carrying two radio frequency (RF) signals with either SSB or TSSB SCM. It is found that nonlinear distortion can be reduced when the frequency difference of two RF signals is /spl sim/ 1 GHz compared with /spl sim/ 2 GHz and higher for both TSSB and SSB SCMs, particularly for large modulation indexes. When the frequency difference of RF signals is /spl sim/ 2 GHz and beyond, either a small modulation index or a medium modulation index combined with a minimum RF of 6 GHz must be used, and a large modulation index cannot be used due to serious nonlinear distortion. Comparison of SSB and TSSB SCMs with uniform distribution of optical signal subcarriers in a dense wavelength-division-multiplexing RoF system with channel spacing of 12.5 GHz shows by simulation that SSB SCM is better (worse) than TSSB SCM for small (large) modulation indexes.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".