An Interior Point Penalty Method for Utility Maximization Problems in OFDMA Networks
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Bibliographic record
Abstract
This paper investigates the non-convexity of utility-based resource allocation problems in orthogonal frequency division multiple access (OFDMA) networks with heterogeneous traffic classes. Efficient transmission in OFDMA networks requires optimal resource allocation to users based on their current channel states. Also, utility-based resource allocation improves the network resource utilization and application level quality of service (QoS) provisioning. However, a major difficulty in using utility-based OFDMA resource allocation schemes is the non-convexity of corresponding optimization problem. In this paper, a continuous optimization technique is proposed to treat the non-convexity. The approach is based on a combination of penalty function methods and interior point methods. Numerical results demonstrate that the proposed approach solves the problem within limited time, and the solutions are close to near optimal solutions obtained by the search algorithm.
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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 | 0.000 |
| 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