Effects of UAV Position Fluctuations on Air-to-Ground mmWave UAV Communications With Multiple Types of Blockages
Bibliographic record
Abstract
Millimeter wave (mmWave)-based uncrewed aerial vehicle (UAV) communication is a promising candidate for future communications. However, hovering UAVs are susceptible to inevitable position fluctuations, while mmWave are highly sensitive to obstacles. Both factors contribute to variations in the system’s quality of service (QoS). Existing studies addressing either mmWave blockages or UAV fluctuations fail to capture their combined effects on QoS. This paper presents a tractable analytical model that establishes a theoretical relationship between UAV fluctuations and mmWave blockages (static, dynamic, and self-blockages). Closed-form expressions for reliable service probability and coverage probability are derived, providing insights into the impact of these combined factors on QoS. Monte Carlo simulations validate the theoretical analysis, showing that small fluctuations (e.g., less than 0.1 m in the studied scenario) have minimal impact on QoS, while larger fluctuations significantly degrade QoS, with various blockages further exacerbating this degradation. While the results may seem intuitive, the derived formulas reveal non-linearities and subtle dependencies, such as varying QoS sensitivity across different ranges of UAV fluctuations and mmWave blockages. Additionally, the analysis identifies feasible UAV placements to enhance the QoS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".