Performance evaluation of UWB signal transmission over optical fiber
Bibliographic record
Abstract
UWB over fiber (UWBoF) technique has been proposed to increase the area of coverage for UWB communication systems. In this paper, the transmission performance of impulse UWB signals over optical fiber is analyzed. Three types of UWB signals generated based on three different techniques are considered. Since optical signals with different optical spectra would have different tolerances to fiber dispersion, the transmission performance of the three types of UWB signals is studied. First, the impact of fiber chromatic dispersion on UWB waveforms and their spectra is evaluated. Then, the transmission performance of data-modulated UWB signals in an optical fiber is investigated, with a general model to analyze the signal power spectral density (PSD) being developed. The PSD of an UWB signal with on-off keying (OOK), bi-phase modulation (BPM) and pulse position modulation (PPM) schemes is calculated. Evolution of the PSD as a function of transmission distance is then performed. The suitability of the three types of UWB signals for UWBoF applications is also evaluated. The study provides a guideline for the design and development of a practical UWBoF system.
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 | 0.000 |
| 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".