Total Cost of Ownership Optimization for Direct Air-to-Ground Communication Networks
Bibliographic record
Abstract
Aircraft cabins are one of the last venues without mobile broadband. Considering future 5G applications and connectivity requirements, direct air-to-ground communications (DA2GC) is the only technique which can provide high capacity and low latency backhaul link for aircraft via a direct communication link. To this end, we propose an analytical framework to investigate the ground station deployment problem for DA2GC network employing multi-user beamforming with dual-polarized hybrid DA2GC antenna arrays. In addition, the proposed framework is utilized to analyze and optimize the total cost of ownership (TCO) of the DA2GC network to provide coverage for European airspace. We present the interplay between different network parameters: the number of ground stations, array size, transmit power and bandwidth, and TCO optimizing deployment parameters are calculated in order to satisfy capacity requirements. At the end, we show that, depending on the cost of different network resources, a terrestrial cellular network can be designed to cover the whole European airspace with limited number of ground stations with a certain array size, i.e., 900 and 361 antenna elements for ground station and air station, respectively.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".