Learning in the Sky: Towards Efficient 3D Placement of UAVs
Bibliographic record
Abstract
Deployment of unmanned aerial vehicles (UAVs) as aerial base stations to support cellular networks can deliver a fast and flexible solution for serving high and varying traffic demand. In order to adequately leverage the benefit of UAVs deployment, their efficient placement is of utmost importance, and requires to intelligently adapt to the environment changes. In this paper, we propose novel learning-based mechanisms for the three-dimensional deployment of UAVs assisting terrestrial networks in the downlink for overloaded situations. The problem is modeled as a game among UAVs. To solve the game, we utilize tools from reinforcement learning, and develop low complexity algorithms based on the multi-armed bandit and satisfaction methods to learn UAVs' locations. Simulation results reveal that the proposed satisfaction based UAV placement algorithm can yield significant performance gains up to about 50% and 41% in terms of throughput and the number of outage users, respectively, compared to a learning based benchmark 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".