An adaptive filtering PN code acquisition scheme with improved acquisition based capacity in DS/CDMA
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Bibliographic record
Abstract
The acquisition-based capacity of a DS/CDMA system is defined as the maximum number of simultaneous users that can achieve PN code acquisition with certain reliability. This paper introduces a novel PN code acquisition scheme utilizing an LMS adaptive filter structure to estimate the delay offset of the desired user in the presence of multiple access interference. It is shown that the adaptive filter significantly outperforms conventional matched filter acquisition schemes. It improves the acquisition-based capacity because it takes into consideration the presence of the multi-user interference while finding the optimum tap-weight setting as opposed to the matched filter which completely neglects the interference. For a PN code length of 127 chips, the matched filter can support only 6 users while the adaptive filter supports up to 40 users at a probability of acquisition failure of 1%. This is essentially the same as the capacity achieved at 10/sup -3/ bit-error-rate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it