A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks
Bibliographic record
Abstract
This comprehensive survey paper examines the applications of artificial intelligence (AI) in unmanned aerial vehicle (UAV)-enabled wireless networks. With the increasing demand for efficient and adaptive communication systems, the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication. The paper first outlines the background and motivation behind AI integration, highlighting the potential for enhanced network performance, autonomy, and adaptability. It then delves into the key AI applications across different network layers, including data sensing and collection, placement and trajectory optimization, radio resource management, routing and topology control, edge computing and caching, as well as security and privacy enhancement. For each application, the paper discusses relevant AI techniques, main findings, optimization objects, and the potential benefits and challenges. The survey also identifies open issues, such as the practical implementation gap, standardization issues, and real-world application barriers, and proposes future directions to address these challenges and further advance the field. In conclusion, the integration of AI with UAV-enabled wireless networks (UWNs) holds tremendous potential for transforming wireless communication, enabling new applications and services with unprecedented capabilities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".