Joint UAV Trajectory and Power Allocation With Hybrid FSO/RF for Secure Space–Air–Ground Communications
Bibliographic record
Abstract
In the coming sixth-generation era, space-air–ground integrated network (SAGIN) is a technology with the potential for seamless coverage and high-data rate transmission. However, the inherent broadcast nature of wireless communication forces us to consider physical-layer security. This article explores secure communications with the aid of hybrid free space optical/radio frequency (FSO/RF) links in a two-phase uplink transmission. Specifically, in the first-phase transmission, a ground device transmits secrecy data to an unmanned aerial vehicle (UAV) via an radio frequency (RF) link, while the UAV emits artificial noise to confuse an eavesdropper. In the second-phase transmission, the UAV sends the secrecy data to a satellite via an FSO link to defend against RF eavesdropping. More specifically, we design two transmission schemes, i.e., slot-based scheme and period-based scheme, which are suitable for transmitting delay-sensitive data and delay-insensitive data, respectively. In order to maximize the average secrecy rate of the system, the trajectory and power allocation of the UAV are jointly optimized. The objective functions of these two schemes are both nonconvex, which are mathematically intractable to tackle by the interior-point method. Therefore, we use block coordinate descent and successive convex approximation techniques to obtain approximate solutions. Numerical results reveal the impact of the UAV trajectory and power allocation optimization on the average secrecy rate during different flight periods in different schemes. In addition, other benchmark schemes are considered for comparison, and the results indicate that our proposed schemes can achieve higher average secrecy rates.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".