Joint prioritized link scheduling and resource allocation for OFDMA-based wireless networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it