A New Modeling Approach for Utility-Based Resource Allocation in OFDM Networks
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Bibliographic record
Abstract
A new modeling approach is proposed for utility- based resource allocation in orthogonal frequency division multiplexing (OFDM) networks with heterogeneous traffic. The spectrum and power of a base station (BS) are allocated to users, in a point to multi-point manner, to maximize the users' aggregate utility. We first model the problem of assigning sub-carriers to the users and the power allocation to the sub-carriers as a mixed integer nonlinear programming (MINLP) problem. The MINLP problem is maximizing a non-concave objective function over a non-convex feasible region that includes some integer variables. We then eliminate integer variables and propose a continuous nonlinear programming (NLP) model for the problem. The obtained model is suitable for heuristic and search algorithms. Genetic algorithm (GA) is applied to obtain the near optimal solution of the NLP model. Numerical results are presented to illustrate the convergence of the GA and utilization performance of the network.
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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