UAV-Enabled Wireless Backhaul Networks Using Non-Orthogonal Multiple Access
Bibliographic record
Abstract
Owing to their potential mobility and agility, unmanned aerial vehicles (UAVs) have captured predominant interests in sustaining 5G wireless communication and beyond. In this paper, we scrutinize the downlink transmission of UAV-enabled wireless backhaul networks in which non-orthogonal multiple access is incorporated to boost up the massive connectivity and high spectra efficiency. More precisely, our aim is to maximize the worst ground user's achievable rate by optimizing bandwidth allocation, UAV's power allocation and placement. The formulated problem is non-convex and not easy to solve optimally. Consequently, to deal with the complexity and non-convexity of our problem, we develop a path following procedure and generate a less-onerous algorithm that is iteratively run till convergence. The simulation results are executed to validate not only the effectiveness, but also the convergence of the proposed method. In addition, a comparison with other alternative schemes is depicted to divulge the outperformance of our proposed algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".