Efficient 3-D Placement of an Aerial Base Station in Next Generation\n Cellular Networks
Bibliographic record
Abstract
Agility and resilience requirements of future cellular networks may not be\nfully satisfied by terrestrial base stations in cases of unexpected or\ntemporary events. A promising solution is assisting the cellular network via\nlow-altitude unmanned aerial vehicles equipped with base stations, i.e.,\ndrone-cells. Although drone-cells provide a quick deployment opportunity as\naerial base stations, efficient placement becomes one of the key issues. In\naddition to mobility of the drone-cells in the vertical dimension as well as\nthe horizontal dimension, the differences between the air-to-ground and\nterrestrial channels cause the placement of the drone-cells to diverge from\nplacement of terrestrial base stations. In this paper, we first highlight the\nproperties of the dronecell placement problem, and formulate it as a 3-D\nplacement problem with the objective of maximizing the revenue of the network.\nAfter some mathematical manipulations, we formulate an equivalent\nquadratically-constrained mixed integer non-linear optimization problem and\npropose a computationally efficient numerical solution for this problem. We\nverify our analytical derivations with numerical simulations and enrich them\nwith discussions which could serve as guidelines for researchers, mobile\nnetwork operators, and policy makers.\n
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".