Performance and stability analysis of buffered slotted ALOHA protocols using tagged user approach
Bibliographic record
Abstract
This paper presents a new approximation approach to analyze slotted ALOHA (S-ALOHA) systems with finite user population having either finite or infinite user buffer capacity. By assuming a symmetric channel, the performance analysis of the overall system is determined by the performance of an arbitrarily selected user, called the tagged user. The service time distribution for the tagged user buffer is found using a state flow graph. This distribution is then applied to the queueing analysis of the tagged user using available classical queueing theory results. The proposed approach can be applied to analysis of systems with a very large user population and user buffer capacity. The distributions and mean values of the important performance indices such as waiting time, queue size, and interdeparture time are obtained. The stability of the system with infinite buffer capacity is also studied. The region of transmission probability p in which the system is always stable and has best performance is obtained. Though the system with finite buffer capacity is considered to be always stable, a comprehensive analysis of the equilibrium points in the system is presented. The analysis presented will allow a proper choice of transmission probability so that the system always operates at the desired equilibrium point.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".