Integration Interval Determination Algorithms for BER Minimization in UWB Transmitted Reference Pulse Cluster Systems
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. Previous research indicated that the integration interval of the auto-correlation detector is critical to the performance of TRPC. Therefore, in this paper, three practical data-aided algorithms are introduced to determine the integration interval of the TRPC structure: the conventional threshold-crossing concept, the new bit error rate (BER) minimization based approach, and the new hybrid scheme that combines threshold-crossing and the BER minimization concepts. The performances of the three schemes are extensively evaluated by simulation. Results show that, the BER minimization based approach and the hybrid scheme demonstrate around 2 dB performance gain over the threshold-crossing scheme in IEEE 802.15.4a channels. Moreover, the hybrid scheme yields close performance to the BER minimization based scheme with much reduced complexity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".