A Retransmission Cut-Off Random Access Protocol with Multi-packet Reception Capability for Wireless Networks
Bibliographic record
Abstract
A retransmission cut-off algorithm for Slotted ALOHA random access protocol is studied in terms of the new packet generation rate, the number of retransmission trials and the Multi-packet Reception capacity. The retransmission cut-off is not needed for a stable operation of Slotted ALOHA if the new packet generation rate is below a critical limit. The values of these critical limits increase almost linearly with the increase of MPR capability. The throughput never reaches its maximum value if the new packet generation rate is less than the corresponding critical limits irrespective of the number of retransmission trials. The maximum throughput is attained by adjusting the number of retransmission trials, pertaining to the new packet generation rate exceeding the corresponding critical limits. A complete analysis for the new packet generation rate with the proper adjustment of the number of retransmission trials and the Multi-packet Reception capability that maximizes the channel throughput and stable operation is found. The stable and unstable operating regions in terms of the new packet generation rate, the number of retransmission trials and the Multi-packet Reception capability is devised. The throughput and packet rejection probability of retransmission cut-off Slotted ALOHA with Multi-packet Reception capability are also provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".