Integration Interval Determination in Transmitted Reference Pulse Cluster Systems for UWB Communications
Bibliographic record
Abstract
A recently proposed transmitted reference pulse cluster (TRPC) structure contains compactly spaced reference and data pulses, and enables a low complexity, robust and practical auto-correlation detector to be used at the receiver. TRPC has been shown to outperform the conventional transmitted reference and non-coherent pulse position modulation systems in ultra-wideband channels. Previous research indicated that the integration interval of the auto-correlation detector is critical to the performance of TRPC. In this paper, practical data-aided algorithms are introduced to determine the integration interval of the TRPC structure based on minimizing the system bit error rate. The proposed scheme is compared to the traditional threshold crossing method and demonstrates around 2 dB performance gain in IEEE 802.15.4a channels. A simplified version of the scheme valid for low signal to noise ratios is also presented. The proposed integration interval determination method does not require Nyquist rate sampling or analog averaging with symbol long delay lines, and is therefore suitable to practical implementation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".