Throughput and PN codephase acquisition for packet CDMA without preamble
Bibliographic record
Abstract
In CDMA packet transmission, it is customary to start each packet with a preamble consisting of pseudo-random (PN) code with no data modulation. The preamble facilitates receiver acquisition of the spreading code alignment and this is essential for decoding the spread spectrum signal. Since packets are short, matched filters are employed to provide rapid acquisition and, in this case, we use a segmented matched filter that can provide codephase alignment even when there is data modulation. We thus eliminate the need for a packet preamble and improve the system throughput. However, if the matched filter fails to provide the correct codephase, a packet is lost. The probability of correct codephase detection (Pd) is increased by accumulating matched filter samples over several code cycles prior to making a decision. Using accumulated code cycles as a parameter, we present the probability of correctly detecting packet codephase as a function of the number of active co- users. Correct detection probabilities exceeding 99% are indicated from simulations with 25 co-users and 10 kHz Doppler shift or carrier frequency offset by accumulating five or more PN code cycles, using maximum selection detection criterion. Analysis and simulation also show that cyclic accumulation can improve packet throughput by 50% and by as much as 100% under conditions of high offered traffic and carrier frequency offset for both fixed capacity and infinite capacity packet systems.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".