Moving Aerial Base Station Networks: A Stochastic Geometry Analysis and Design Perspective
Bibliographic record
Abstract
Recently, the utilization of aerial base stations (ABSs) has attracted a lot of attention. For the static implementation of ABSs, it has been shown that if the ABSs are statistically distributed in a given height over a cell, according to a binomial point process (BPP), a fairly uniform coverage across the cell is achievable. However, such a static deployment exhibits poor performance in terms of average fade duration (AFD) for the static or low speed moving users and power consumption. Therefore, considering a network of moving ABSs is of practical importance. On the other hand, once such a moving ABS network is considered, the coverage probability may not necessarily remain at an acceptable level. This paper is concerned with the design of stochastic trajectory processes such that if according to which the ABSs move, in addition to improving the AFD, an acceptable coverage profile can be obtained. We propose two families of such processes, namely, spiral and oval processes, and analytically demonstrate that the same coverage as the static case is achievable. We then focus on two special cases of such processes, namely, radial and ring processes, and show that the AFD is reduced about two orders of magnitude with respect to the static case. To obtain a more practical scenario, we also consider deterministic counterparts of the proposed radial and ring processes and show that similar coverage and AFD as the stochastic case can be obtained.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".