Fitness landscape analysis for resource allocation in multiuser OFDM based cognitive radio systems
Why this work is in the frame
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Bibliographic record
Abstract
Cognitive Radio (CR) is a promising technique for improving the spectrum efficiency in future wireless communication networks. In this paper, dynamic resource allocation in a Multiuser Orthogonal Frequency Division Multiplexing (MU-OFDM) based CR system is investigated. Dynamic resource allocation in MU-OFDM CR systems is a computationally complex combinatorial optimization problem. Memetic algorithms (MAs), which are hybrid evolutionary algorithms with local searches, have been shown to outperform traditional algorithms for many combinatorial optimization problems. However, the performance of MAs is highly dependent on the choice of the local search and evolutionary operators. This choice should be based on the characteristics of the problem at hand. Fitness landscape is an important technique for analyzing the behavior of combinatorial optimization problems. Based on fitness landscape analysis, appropriate local search and evolutionary operators are selected for the proposed MA. Simulation results show that the proposed memetic algorithm provides better performance than existing algorithms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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