Scheduling for long term proportional fairness in a cognitive wireless network with spectrum underlay
Bibliographic record
Abstract
In this paper we study fair rate scheduling in an ad hoc cognitive wireless network with spectrum underlay. Transmissions in the network are allowed provided their interference to the primary network is below a predefined threshold. An optimal scheduling problem is formulated with an objective to achieve proportional fairness (PF) of the long-term average transmission rates among different links. Implementing the optimum scheduling requires high complexity. Two practical scheduling schemes are then proposed. In the first scheme, transmission priorities of the links are determined by their potential contributions to an objective utility function, assuming there is no co-channel interference within the network. In the second scheme, transmission priorities are derived from both the objective function and interference to the primary network. We also consider using exclusive regions to limit interference among simultaneous transmissions in order to improve the system throughput. The scheduling schemes can be implemented distributively in the ad hoc cognitive wireless network with limited assistance from the primary network. Our results show that the proposed PF scheduling schemes can achieve high overall throughput and close-to-optimum fairness, and using exclusive regions can improve the system utility without compromising the fairness performance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".