Hybrid ARQ and optimal signal—to—interference ratio assignment for high—quality data transmission in DS—CDMA
Bibliographic record
Abstract
Abstract Combined error correction and detection techniques, or hybrid automatic—repeat—request (ARQ) schemes with negative acknowledgment are considered for highly reliable reverse link data transmission in direct sequence code division multiple access (DS—CDMA) personal communication systems. The proposed selective repeat hybrid ARQ scheme employs an inner convolutional code for error correction and an outer shortened BCH code for error detection. Each negative acknowledgment message contains all packet sequence numbers that have been detected in error, and it is both error—correction and error—detection encoded. Accepted packet error rate and throughput are derived as a function of the error correction encoded frame error rate. An optimum signal—to—interference ratio (SIR) assignment maximizing the system's capacity is also found. A comprehensive simulation is conducted to evaluate performance of the data transmission system. Analysis and simulations show that by applying the proposed type I hybrid ARQ scheme with properly selected system parameters, virtually error free and highly efficient data transmission in CDMA personal communication systems is possible.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".