Towards Cloud-Native RAN for 6G NTN-Based Connectivity in Aviation
Bibliographic record
Abstract
The aviation sector is emerging as a key vertical in the development of 6G Non-Terrestrial Networks (NTN), driven by increasing demand for high-capacity and low-latency in-flight connectivity (IFC). In this work, we explore the potential of disaggregated and cloud-native Radio Access Network (RAN) architectures to support seamless gate-to-gate connectivity for commercial aircraft. We propose and analyze four deployment scenarios that distribute the functions of disaggregated gNB, the central unit (CU), the distributed unit (DU), and the radio unit (RU) across aircraft, satellite platforms, and ground infrastructure. Emphasizing latency budget considerations, we examine trade-offs between performance, scalability, and complexity across multi-orbit satellite constellations. We further argue that adopting cloud-native RAN principles, along with intelligent orchestration and AI-based adaptation, can enable flexible and resilient architectures for future 6G NTN-based aviation services.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".