Resource optimization in UAV‐assisted wireless networks—A comprehensive survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Unmanned aerial vehicles (UAVs) are inevitable to meet the requirements of future wireless networks. Recently, researchers have investigated diverse issues related to UAV‐assisted networks (including placement of UAVs, resource management, and spectrum sharing) for a broad range of applications, including disaster management, data collection from the ground sensor network, surveillance, logistic support, etc. This article presents a comprehensive survey of recent advances in UAV‐assisted networks. We mainly emphasize the optimization perspective of UAV‐assisted wireless networks with different objectives, including coverage area, throughput, energy efficiency, quality of service, delay, and outage probability. We provide a detailed discussion for each objective with their constraints, optimization problem, solution approach, and performance metrics. We also provide relationships among different objectives and parameters considered in the literature. Finally, we list open research issues and future research directions to improve UAV‐assisted wireless networks in the context of optimization.
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.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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