Fitness landscape analysis for resource allocation in multiuser OFDM based cognitive radio systems
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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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 | 0.003 |
| 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 | 0.000 |
| 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".