Ultra-wideband pulse shaping: bypassing the inherent limitations of the Gaussian monocycle
Bibliographic record
Abstract
UWB impulse radio transmissions are power limited as they must respect a frequency mask with low total permitted power and severe isolation requirements for the GPS band. Shaping of the short UWB impulse maximizes transmit power subject to these constraints. We have previously published results for intricate pulse shaping in the optical domain that achieves record levels of power transmission. The most successful pulse shaping techniques in the electrical domain have been achieved with derivatives of the Gaussian pulse shape. The Gaussian monocycle is a low performance choice in terms of transmit power, but nonetheless the subject of intense research activity. In this paper we compare and contrast these UWB pulses, and quantitatively compare their performance in realistic systems (specific RF and antenna hardware). There is the perception that failings in the pulse shape (especially for the monocycle) can be compensated by simple highpass filter and the bandpass nature of the UWB antenna. We fabricate and characterize two UWB antenna designs available in the literature, as well as one commercial antenna. We use our optical pulse shaping device to experimentally investigate the three different pulse shapes and measure their transmitted power spectral density with each of the UWB antennas. We find that the monocycle is significantly less powerful than the others. The fine pulse shaping available in optical processing provides 1.7 to 2.9 dB gain over the electrical processing methods, depending on the antenna used.
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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) | 0.001 | 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".