UAV-Assisted Wireless Backhaul Networks: Connectivity Analysis of Uplink Transmissions
Why this work is in the frame
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Bibliographic record
Abstract
With the proliferation of wireless communication technologies, user equipments (UEs) in rural or disaster areas have data-transmission demand to upload their data to the core network. However, current networks lack coverage in rural or disaster areas due to the absence or damage of/to infrastructures. To address this issue, a promising solution is employing unmanned aerial vehicles (UAVs) as relays to assist the wireless backhaul of UEs to remote ground base stations (GBSs). For convenience, we call these networks as UAV-assisted wireless backhaul networks (UABNs). This paper aims to investigate the uplink transmission performance in UABNs. In particular, we analyze the connectivity of the two-hop uplink path from a reference UE to a remote GBS via a reference UAV. Compared with previous studies that mostly analyze single-hop transmissions, the investigation of the path connectivity of UABNs is more complex because of the location variation of UAVs as well as the complexity of the interference at the two-hop path. Considering the distribution of UEs, we exploit stochastic geometry to establish a theoretical model to analyze the path connectivity of UABNs. In our model, UEs form clusters according to a Poisson Cluster Process (PCP) and one UAV serves one UE cluster. Based on our model, the connectivity of a two-hop uplink path is finally derived by limiting the signal-to-noise-plus-interference (SINR) above a threshold. Theoretical values of the connectivity of UABNs match with simulation results, confirming the accuracy of the proposed analytical model. Our results also offer insightful implications for constructing and configuring UABNs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it