A Cognitive Radio Enabled RF/FSO Communication Model for Aerial Relay Networks: Possible Configurations and Opportunities
Bibliographic record
Abstract
Two emerging technologies, cognitive radio (CR) and free-space optical (FSO) communication, have created much interest both in academia and industry recently as they can fully utilize the spectrum while providing cost-efficient secure communication. In this article, motivated by the mounting interest in CR and FSO systems and by their ability to be rapidly deployed for civil and military applications, particularly in emergency situations, we propose a CR enabled radio frequency (RF)/FSO communication model for an aerial relay network. In the proposed model, CR enabled RF communication is employed for a ground-to-air channel to exploit the advantages of CR, including spectrum efficiency, multi-user connectivity, and spatial diversity. For an air-to-air channel, FSO communication is used, since the air-to-air path can provide perfect line-of-sight connectivity, which is vital for FSO systems. Finally, for an air-to-ground channel, a hybrid RF/FSO communication system is employed, where the RF communication functions as a backup for the FSO communication in the presence of adverse weather conditions. The proposed communication model is shown to be capable of fully utilizing the frequency spectrum, while effectively dealing with RF network problems of spectrum mobility and underutilization, especially for emergency conditions when multiple unmanned aerial vehicles (UAVs) are deployed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".