Sub-channel and power allocation for multiuser OFDM with rate constraints using Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
We demonstrate that the resource allocation problem in OFDM (for which there are no complete analytical solutions or numerical solutions that are practical) can be solved in real time using the Genetic Algorithm (GA). The sub-channel assignment and power allocation that maximize the throughput of the system with constraints on total power usage and users' transmission rates are obtained using an intelligent search based on GA. Our version of the GA uses two chromosomes per individual - one for the channel assignment and another for the power allocation. Users' transmission rate constraints are met by awarding points to individuals who satisfy the constraints and incorporating the points into the fitness function. There is no analytical method that produces the global optimum solution to the problem on its complete form with the constraints mentioned above for us to compare our result with. However, by comparing our solutions to the existing global optimum solutions for the cases with less constraints, we show that our algorithms produce results that are within 5% of the global optimum.
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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