Content Delivery Analysis in Cellular Networks With Aerial Caching and mmWAVE Backhaul
Bibliographic record
Abstract
In this paper, we investigate the successful content delivery (SCD) performance in the unmanned aerial vehicle (UAV) integrated terrestrial cellular networks, where the caching-enabled UAVs are dispatched to offload the burst traffic from the cellular networks. Specifically, the UAV and terrestrial cellular network share the same spectrum resources for user downlink communications and each UAV uses millimeter wave (mmWave) communications for self-backhaul. We derive a closed-form expression of the achievable rate of the mmWave wireless backhaul link and then analyze the minimum cache hit probability to achieve a certain backhaul rate requirement. By approximating the general probabilistic line-of-sight (LoS) propagation model as a LoS ball model, we analyze the conditional SCD probabilities by leveraging stochastic geometry tools. Simulation results demonstrate that the UAV integrated cellular network can not only provide more access opportunities but also achieve higher SCD performance than the conventional terrestrial network for ground users. Moreover, there exists an optimal UAV density and height to maximize the SCD performance. Our results provide useful guidelines for the design and deployment of future UAV-assisted networks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".