Spectrum and Operational Efficiency Optimization Using Airborne Communication Network Capacity Modeling for Cognitive Radios
Bibliographic record
Abstract
The radio spectrum is one of the most important elements in current communications; nevertheless, it is a limited resource and must be used in a responsible way to host emerging technologies. These technologies include Airborne Communication Networks (ACN), which has attracted much attention of researchers and industry over the last years. The drivers of ACN are mainly to fulfill the mobile communications requirements from passengers, and support the constant growth in aeronautical communications needs. In this paper, requirements of communication channel capacity for future airborne network are studied in order to simulate an airborne network in North Atlantic Tracks (NAT), which cover up to 80% of all oceanic and aerial traffic. To this end, the main aircraft models that fly over this air space as well as the estimation of the future capacity channel of each aircraft are contemplated in order to reduce the spectrum demand and optimize the operational efficiency in the ACN. This paper also presents the use of cognitive radios for the implementation of ACN due to its flexibility for switching to other wireless protocols, integrating new standards in aircraft without substantial cost, developing monitoring tools to guarantee QoS, processing signals for more efficiently use of spectrum and ensuring the scalability and reconfigurability of system. Finally, the simulations results obtained will be used to determine the dimensions of the ACN in terms of number of frequencies and channel capacity, as well as its implementation in cognitive radio on board aircraft
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".