Distributed and Multilayer UAV Networks for Next-Generation Wireless Communication and Power Transfer: A Feasibility Study
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) for wireless communications have rapidly grown into a research hotspot as the mass production of high-performance, low-cost, and intelligent UAVs becomes practical. In the meantime, the fifth generation (5G) wireless communication and Internet-of-Things (IoT) technologies are being standardized and planned for global deployment. During this process, UAVs are becoming an important part of 5G and IoT, and expected to play a crucial role in enabling more functional diversity for wireless communications. In this paper, we first present a summary of mainstream UAVs and their use in wireless communications. Then, we propose a hierarchical architecture of UAVs with multilayer and distributed features to facilitate the integration of different UAVs into the next-generation wireless communication networks. Finally, we unveil the design tradeoffs with the consideration of power transfer, wireless communication, and aerodynamic principles. In particular, empirical models and published measurement data are used to analyze power transfer efficiency, and meteorological impacts on UAVs enabled next-generation wireless communications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".