Modeling and Analysis of Aerial Base Station-Assisted Cellular Networks in Finite Areas Under LoS and NLoS Propagation
Bibliographic record
Abstract
Aerial base station (ABS) provides a flexible solution for hotspot scenarios in traditional cellular networks, where macro-cell base stations (MBSs) are challenged by overwhelming short-time traffic demands. However, due to stretched transmission distances from sky, the system performance of this ABS-scheme is sometimes questioned. In this paper, we study the system performance of ABS-assisted networks by tools from stochastic geometry. The two-tier network consisting of MBSs and ABSs is modeled as the superposition of a Poisson point process over the infinite plane and a binomial point process within an overlapped finite circular area. We consider a more general probabilistic line-of-sight and non-line-of-sight propagation model and derive coverage probability as well as area spectral efficiency. Based on the proposed analytical framework, we study the impacts of various parameters and compare the ABS-scheme with a benchmark scheme, in which network densification is realized through deploying additional ground base stations (GBSs). Simulation results unveil that: 1) the height and ABS number should be carefully designed to obtain the optimal performance and 2) when the number of assisting BSs is limited, the ABS-scheme can achieve even better performance than the GBS-scheme, which validates the feasibility of enhancing system performance through ABSs in hotspot scenarios.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".