EDA-based joint power, subcarrier allocation and relay assignment scheme for multiuser relaying in OFDMA-based cognitive radio systems
Bibliographic record
Abstract
In this paper, we present an interference and channel conditions aware multiple relay assignment and subcarrier allocation scheme for OFDMA based cognitive radio systems employing cooperative transmission using Decode and Forward (DF) technique. We focus on the problem of assigning the relays, the relay powers and allocating the subcarriers to the destination nodes using the sum capacity of the cognitive radio system as the objective function to be maximized under the constraint of maximum acceptable levels of interference for the primary users (PU). The computational complexity of this assignment using exhaustive search algorithm grows exponentially with the number of relays, subcarriers and receivers. Thus we propose Estimation of Distribution Algorithm (EDA) for this assignment problem. EDA is a probabilistic evolutionary algorithm which updates its population at each iteration on the basis of the probability densities obtained from the population of superior candidates evaluated and chosen at the previous iteration. EDA employed with our allocation scheme has low computational complexity and its performance is comparable to the Upper Bound of the solution as shown by various simulation results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".