Placement and Power Allocation for NOMA-UAV Networks
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) can be used as flying base stations to provide ubiquitous connections for mobile devices in over-crowded areas. On the other hand, non-orthogonal multiple access (NOMA) is a promising technique to support massive connectivity. In this letter, the placement and power allocation (PA) are jointly optimized to improve the performance of the NOMA-UAV network. Since the formulated joint optimization problem is non-convex, the location of the UAV is first optimized, with the total path loss from the UAV to users minimized. Then, the PA for NOMA is optimized using the optimal location of the UAV to maximize the sum rate of the network. Simulation results are presented to show the effectiveness and efficiency of the proposed scheme for NOMA-UAV networks.
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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 | 0.000 | 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 it