Aerial Base Stations for Global Connectivity: Is it a Feasible and Reliable Solution?
Bibliographic record
Abstract
Even though achieving global connectivity represents one of the main goals of 5G and beyond wireless networks, exurban areas are still suffering frequent outages due to the lack of proper telecom infrastructures, which are often available only in urban areas. Indeed, cellular network design is usually capacity-driven, and thus base stations’ densities mostly follow population and especially revenue densities. Contextually, we focus on one of the most promising solutions to provide sufficient and reliable coverage in far-flung areas: aerial base stations, which consist of unmanned aerial vehicles carrying cellular base station equipment. In this paper, we extensively discuss the problem of bridging the so-called urban-rural digital divide (i.e., the connectivity gap between urban and rural areas) from various perspectives. First, we showcase various alternative solutions, and compare conventional terrestrial networks with aerial networks from a techno-economic point of view. Then, we highlight the topological aspects of rural environments and explain how they can affect the actual design of cellular networks. In addition, we investigate both the coverage probability and the reliability of the communication links via simulations, proving that the integration of aerial base stations can be quite promising in a 6G perspective. Finally, we propose two original extensions of our case study as open problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".