Low-Complexity Priority-Aware Interference-Avoidance Scheduling for Multi-user Coexisting Wireless Networks
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Bibliographic record
Abstract
In this paper, the priority-aware interference-avoidance scheduling for multi-user coexisting wireless networks with heterogeneous traffic demands is addressed. Both admission control and throughput maximization for admitted users are studied. These problems are addressed by a proposed sequential solution framework where at each step a large-scale linear program with a large number of variables is required to be solved. To efficiently solve the large-scale program, an accelerated column generation based method is proposed. In the proposed method, an efficient greedy initialization algorithm is first put forward by exploiting the proposed solution structure. After that, both upper and lower bounds on the optimal objective function of each optimization problem are derived, which are used to significantly alleviate the dependence of the whole solution procedure on deriving optimality of problems. Simulation results show that the proposed algorithm can effectively and efficiently handle the coexistence of multiple users with heterogeneous priorities and traffic demands.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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