Bibliographic record
Abstract
This paper applies a segmented matched filter for acquiring chip codephase alignment in direct-sequence CDMA packet communications. This alignment is essential for decoding the spread spectrum signal. The customary approach is to start each CDMA packet with a data-free preamble that facilitates codephase alignment in the receiver. Since packets can be short, processing must be rapid and so we use the parallel processing of a transversal matched filter correlator. We include segmentation of the matched filter to allow codephase testing even when there are data transitions. This eliminates the need for a preamble and the entire packet can contain data. Segmentation also increases tolerance to Doppler shifts caused by motion or by carrier frequency mismatch. To increase the probability of correct codephase acquisition, matched filter samples are accumulated over several code cycles prior to making the codephase decision. Correct detection probabilities exceeding 99% are indicated from simulations with 25 co-users and 10 kHz Doppler shift
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".