Fairness Assessment of the Adaptive Token Bank Fair Queuing Scheduling Algorithm
Bibliographic record
Abstract
Adaptive token bank fair queuing (ATBFQ) algorithm has been proposed as a cross-layer scheduling technique for 4G wireless systems recently. This algorithm takes higher layer quality of service (QoS) attributes such as priorities, interflow fairness, and delay constraints into account. By selecting the user terminals (UTs) in a certain prioritized manner derived from QoS attributes, the performance of the UTs, suffering from high interference and/or shadowing in particular, can be improved. The ATBFQ algorithm has been tested in a multicell environment in the presence of intercell interference by comparing with reference Score Based (SB) and Round Robin (RR) algorithms. In this paper, we further analyze ATBFQ primarily with regard to fairness along with other performance metrics accessed in a more elaborate system considering varying interference and loading conditions. Furthermore, an adaptive method for the allocation of resources is proposed for ATBFQ parameter selection, and is shown to have better performance in various loading conditions. It is observed from simulation results that ATBFQ with adaptive parameter selection outperforms the reference schemes in terms of queuing delay and UT throughput for different network loading cases.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".