Effects of Blockage in Deploying mmWave Drone Base Stations for 5G Networks and Beyond
Bibliographic record
Abstract
Due to their unconstrained mobility and capability to carry goods or equipment, unmanned aerial vehicles (UAVs) or drones are considered as a part of the fifth-generation (5G) wireless networks and become attractive candidates to carry a base station (BS). As 5G requirements apply to a broad range of uses cases, it is of particular importance to satisfy those during spontaneous and temporary events, such as a marathon or a rural fair. To be able to support these scenarios, mobile operators need to deploy significant radio access resources quickly and on demand. Accordingly, by focusing on 5G cellular networks, we investigate the use of drone-assisted communication, where a drone is equipped with a millimeter-wave (mmWave) BS. Being a key technology for 5G, mmWave is able to facilitate the provisioning of the desired per-user data rates as drones arrive at the service area whenever needed. Therefore, in order to maximize the benefits of mmWave-drone-BS utilization, this paper proposes a methodology for its optimized deployment, which delivers the optimal height, coordinates, and coverage radius of the drone-BS by taking into account the human body blockage effects over a mmWave-specific channel model. Moreover, our methodology is able to maximize the number of offloaded users by satisfying the target signal quality at the cell edge and considering the maximum service capacity of the drone-BS. It was observed that the mmWave-specific features are extremely important to consider when targeting efficient drone-BS utilization and thus should be carefully incorporated into analysis.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".