Packet-Level Throughput Analysis and Energy Efficiency Optimization for UAV-Assisted IAB Heterogeneous Cellular Networks
Bibliographic record
Abstract
In this paper, we investigate the packet-level throughput and energy efficiency of millimeter-wave unmanned aerial vehicle (UAV)-assisted integrated access and backhaul (IAB) heterogeneous cellular networks with spatiotemporal traffic. Specifically, we develop a theoretical framework to analyze the mean packet throughput and energy efficiency of the network based on stochastic geometry and queueing theory, whereby the spatial randomness of network deployment and the temporal randomness of network traffic can be appropriately characterized. Different from the traditional network performance metrics emphasizing transmission and resource consumption, the packet-level performance helps to better understand the impact of not only packet transmission but also packet waiting time in a multihop network. Simulation results demonstrate that the assistance of IAB-based UAVs can efficiently relieve the load of terrestrial macro- and small-cell networks, and the appropriate network deployment parameters play pivotal roles in improving both the packet-level throughput and energy efficiency performance. By jointly optimizing the key network parameters, the packet energy efficiency can be significantly improved while ensuring the required mean packet throughput of the network.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".