UAV-Assisted Wireless Backhaul Networks: Connectivity Analysis of Uplink Transmissions
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".