Environment-Aware Drone-Base-Station Placements in Modern Metropolitans
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicles, i.e., drones, have recently caught attention for providing on-demand capacity to wireless networks as drone-base-stations (drone-BSs). Many studies assume simplified channel models based on average characteristics of the environment to estimate the placement of drone-BSs. However, especially in urban areas, positioning of drone-BSs with respect to intersections and roof-top heights of buildings can severely change the path loss characteristics. To address this issue, we adopt an ITU channel model utilizing more information about the environment, such as the shapes of the buildings. We optimize parameters of the selected ITU model, so that it can be used for altitudes both strictly lower and higher than building roof-tops. Using ray-tracing simulations as a benchmark, we compare the proposed model with a widely used simpler model. Results show that the proposed model can reduce the root-mean-squared error from 35 to 10 dB, which may have critical implications for drone-BS operations, such as planning for the required number of drone-BSs to cover outdoor urban users, as demonstrated with simulations.
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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 | 0.001 | 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