A New Modeling Approach for Utility-Based Resource Allocation in OFDM Networks
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".