Empowering Non-Terrestrial Networks with Artificial Intelligence: A Survey
Bibliographic record
Abstract
The fifth generation (5G) of wireless communication technology has revolutionized the way we connect with each other; enabling faster data transfer rates, lower latency, and higher reliability.However, the demand for even more efficient and ubiquitous connectivity is ever-growing.As such, researchers have already been exploring the potential of the sixth generation (6G) of wireless communications.The 6G networks are expected to provide unprecedented connectivity and reliability.An important component of 6G networks is the use of non-terrestrial networks (NTNs) that have the potential to extend the coverage of 6G networks to areas where connectivity with traditional terrestrial networks is not feasible or cost-effective.NTNs include technologies with low earth orbit (LEO) satellites, high-altitude platforms (HAPs), and unmanned aircraft systems (UASs).However, the deployment and management of NTNs face numerous technical and operational challenges, such as network planning, resource allocation (RA), and interference management.Recent advances in artificial intelligence (AI) offer new opportunities for optimizing the performance of NTNs.By leveraging AI techniques, such as machine learning (ML), deep learning (DL), and reinforcement learning (RL), network operators can enhance the efficiency, reliability, and security of NTNs in 6G wireless communications.This survey paper comprehensively reviews the state-of-the-art research on AI-powered NTNs for 6G wireless communications.It covers key NTN technologies, AI techniques for network optimization, and recent advances in AI-powered NTNs, as well as challenges and opportunities for future research.The paper also discusses the potential impact of advancing AI on the development of 6G networks and beyond.The findings of this survey paper deliver valuable insights for researchers, practitioners, and policy-makers in the field of 6G networks and AI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".