Optimal Random Access Strategies for Trigger-Based Multiple-Packet Reception Channels
Bibliographic record
Abstract
This paper focuses on trigger-based (TB) random access (RA) strategies for a multiple-packet reception channel with channel capability <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula>-MPR channel), where up to <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> packets can be received simultaneously, while more than <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> concurrent packet transmissions result in collisions and are considered lost. We model the contention for the TB MPR framework and derive the optimal RA strategies that maximize two metrics: <i>i)</i> the normalized saturation throughput, and <i>ii)</i> the number of stations successfully occupying the MPR channel within each access round. We generalize the <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-persistent carrier sense multiple access (CSMA) by enabling it to explore both the MPR dimension and the time dimension to adapt the access probabilities. We also propose suboptimal strategies to reduce the complexity, customized for the considered TB framework. Comprehensive performance evaluations and comparisons with respect to a wide range of system parameters and metrics are provided.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".