A multiobjective analytic framework for slotted ALOHA wireless LANs
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Bibliographic record
Abstract
This paper presents a multiobjective analytic framework for the study of individual performance requirements in a multi-class slotted ALOHA wireless local area network (WLAN). The capture effect has a significant impact on network performance in terms of contributing to the overall throughput. However, unfairness may be created when users of different characteristics are present. The proposed framework, which is based on several game theoretic approaches, attempts to address the issues of optimization and fairness jointly. Various concepts of optimality are introduced, in which the criteria of fairness are embedded. A finite-population network model and a fixed-position capture model are established, and the performance measures of the network are evaluated. Examples are given for the design of strategies regarding transmission probabilities based on the multiobjective approach.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it