Resource assignment for adaptively modulated interconnected WLANs
Bibliographic record
Abstract
In this paper we propose resource allocation schemes for TDMA/TDD based high performance link adaptable wireless LANs and examine their interrelation with user buffer. User rates are dynamically adjusted based on their distance from the access points and changes as user moves. We simulate three resource allocation policies namely Minimum Overhead Round Robin (MORR), which does not depend on userpsilas buffer condition, Weighted Minimum Overhead Round Robin (WMORR) which is a function of user buffer as well as the waiting time for transmission opportunity and Weighted Round Robin (WRR) which is a function of user buffer only. We evaluate average buffer packet drop probability due to buffer overflow and average frame overhead in uplink phase of the TDMA/TDD frame. Our results show that the second adaptive resource allocation technique i.e. WMORR outperforms the other two.
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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 | 0.000 |
| 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 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".