Joint Attitude and Power Optimization for UAV-Aided Downlink Communications
Bibliographic record
Abstract
In this paper, we investigate the unmanned aerial vehicle (UAV)-aided communications, where a UAV as an aerial base station (BS) transmits data to multiple ground terminals (GTs) simultaneously and an antenna array is equipped on the UAV. Note that in practice, the spatial resolution of antenna array varies with different directions. Thus, given one user distribution, the direction of antenna array on UAV may be optimized to support the best multiuser spatial separation. Based on this observation, we propose to maximize the minimum throughput of all GTs by jointly optimizing the attitude of UAV and the transmit power for each GT, where the attitude includes both location and direction information. We develop two efficient sub-optimal solutions for this non-convex problem. The interference among the co-scheduled GTs is dramatically reduced through direction adjustment of antenna array. Finally, simulation results are provided to verify the proposed algorithms.
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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".