Joint prioritized link scheduling and resource allocation for OFDMA-based wireless networks
Bibliographic record
Abstract
In this paper, we study the joint prioritized link scheduling and resource allocation for the OFDMA-based wireless network which serves two classes of user links, namely non-prioritized (low-priority) and prioritized (high-priority) links. Our design objectives are to maximize the number of non-prioritized links to be scheduled and to maximize the weighted sum rate of all scheduled links while guaranteeing the minimum rate requirements of all prioritized links. To solve this problem, we first transform the original problem into a singlestage optimization problem which is a Mixed Integer Nonlinear Program (MINLP). Then, we propose an iterative algorithm to solve the transformed problem where we sequentially perform modified power allocation and link removals. We prove the convergence and characterize important properties of the proposed algorithm. Numerical results show that the proposed algorithm significantly outperforms the greedy uniform power allocation and the rounding-based admission algorithm in term of the average number of scheduled non-prioritized links and the weighted sum rate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".