Throughput Upper-Bound of Slotted CSMA Systems with Unsaturated Finite Population
Bibliographic record
Abstract
In this paper we propose a new Markovian model for p-persistent carrier sense multiple access (CSMA) systems with a finite population of unsaturated single-buffered terminals. Focused on the distribution of the number of backlogged terminals in the steady state, our model allows the optimal persistent probability p from the number of backlogged terminals, which enables us to determine the throughput upper-bound (or mean access delay lower-bound) of slotted CSMA systems. We compare the performance of slotted CSMA systems with binary exponential backoff (BEB) algorithm and with p-persistent protocol against the throughput upper-bound and examine the stability of these systems. We show how closely slotted CSMA systems with BEB algorithm or p-persistent protocol approaches the throughput upper-bound in accordance with the minimum contention window size or the persistent probability p. Further, we propose a generalized Bertsekas' (backoff) algorithm (GBA) based on backlog size estimation, which is a generalization of the existing algorithm proposed by Bertsekas, in order to achieve the throughout upper-bound. Our study shows that in slotted CSMA systems, the access fairness of BEB algorithm is worse than those of p-persistent protocol and GBA algorithm, while the BEB and GBA algorithms show throughput performance close to optimality.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".