QoS-Compliant 3-D Deployment Optimization Strategy for UAV Base Stations
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are being integrated as an active element in 5G and beyond networks. Because of their flexibility and mobility, UAV base stations (UAV-BSs) can be deployed according to the ground user distributions and their quality-of-service (QoS) requirement. Although there has been quite some prior research on the UAV deployment, no work has studied this problem in a 3-D setting and taken into account the UAV-BS capacity limit and the QoS requirements of ground users. Therefore, in this article, we focus on the problem of deploying UAV-BSs to provide satisfactory wireless communication services, with the aim to maximize the total number of covered user equipment subject to user data-rate requirements and UAV-BSs' capacity limit. First, we model the relationship between the air-to-ground path loss (PL) and the location of UAV-BSs in both horizontal and vertical dimensions, which has not been considered in previous works. Unlike the conventional UAV deployment problem formulation, the 3-D deployment problem is decoupled into a 2-D horizontal placement and altitude determination connected by PL requirement and minimization. Then, we propose a novel genetic algorithm-based 2-D placement approach in which UAV-BSs are placed to have maximum coverage of the users with consideration of data rate distribution. Finally, numerical and simulation results show that the proposed approach has enabled a better coverage percentage comparing with other schemes.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".