Frequency allocation for green multiuser OFDM systems using evolutionary algorithm
Bibliographic record
Abstract
Evolutionary approaches are usually shunned by engineers in real time applications because of high computational complexity. This paper makes a convincing argument that in computationally challenging problems like energy efficiency (EE) maximizing channel allocation, genetic algorithm (GA) is well worth considering. A two-step solution to the problem of finding the subchannel and power allocation that maximizes the EE of the OFDMA transmissions, under minimum rate and total power constraints, is presented. GA is used for subchannel allocation and is followed by optimal power allocation obtained via analytical methods. The fitness function necessary for the GA is the maximum EE for a fixed channel allocation, and is computationally expensive to be of any use here. Two different closed form approximations to the maximum EE, obtained from our previous work, are used as fitness functions. A new measure of computational complexity for evolutionary algorithms is introduced. Simulation results are used to show that while GA has comparable complexity to the best EE maximizing channel allocation protocol in the literature, it produces nearly double EE.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".