Multi‐objective resource allocation in multiuser orthogonal frequency division multiplexing system
Bibliographic record
Abstract
This study presents a new technique for resource allocation in multiuser orthogonal frequency division multiplexing systems. The goal is to maximise the minimum data rate available to any user while minimising the total transmitted power. The strength Pareto evolutionary algorithm (SPEA‐2) is used to achieve this goal. The SPEA‐2 algorithm solves the contradicting multiple objectives by evaluating individual's fitness value based on the number of external non‐dominated individuals that dominate it and then searching the solution space to minimise this fitness value. Most of the existing multi‐objective solutions, for the problem under consideration, have used binary coded chromosomes which restricted the number of users to be in power of two only. This limitation is overcome in the proposed scheme by using an integer coded chromosome. The population density information is also incorporated into the fitness function to refine the search. Simulation results indicate that the proposed algorithm achieves higher data rates as compared with previous algorithms. Furthermore, the proposed scheme allocates both subcarriers and bits jointly, without being computationally expensive. The faster convergence of the algorithm to near‐optimal value, as compared with previous algorithms is indicative of its reduced complexity, which is attributed to the modification in the power objective.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".