Relay Selection Algorithm for Air–Ground Cooperative Communication Based on System Capacity and Joint User Equalization
Bibliographic record
Abstract
Cooperative communication networks employing Unmanned Aerial Vehicles (UAVs) as aerial relays can significantly enhance user communication quality and extend transmission range. Within such networks, an effective relay selection strategy is crucial for improving the user data rate. To balance system capacity with user fairness, this paper proposes a Scatter Search Relay Selection (SSRS) algorithm based on a network formation game model. The objective is to identify the optimal relay selection strategy for a cooperative communication system involving multiple UAV relays and multiple user pairs. The effectiveness and scalability of the proposed SSRS algorithm are evaluated through simulation experiments. Results demonstrate that SSRS outperforms other mainstream algorithms in terms of both system capacity and user fairness. In summary, the proposed algorithm provides a viable strategy for relay selection, thereby enhancing overall communication quality while ensuring fairness among users.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".