Physical Layer Security in Cybertwin-Enabled Integrated Satellite-Terrestrial Vehicle Networks
Bibliographic record
Abstract
In this paper, we investigate the secure vehicle communications in cybertwin-enabled integrated satellite-terrestrial networks, where the digital twins (DTs) in the cybertwin space reflects the physical entities (i.e., satellite, terrestrial base station (BS), and vehicles). Particularly, considering the channel similarity between different satellite links versus the randomness difference in terrestrial links, it is challenging to reach the secure transmission in satellite and terrestrial links independently with limited resources. Considering the information exchange in the cybertwin space can support an information sharing between such physical entities, the secure transmission design by using the heterogeneous satellite-terrestrial resources can be conducted from a global perspective. With the channel feedback information of vehicles gathered at the cybertwin, the co-channel interference caused by the spectrum sharing is leveraged to assist the implementation of secure transmissions in the integrated satellite-terrestrial vehicle network. Specifically, the problems of maximizing the secrecy rate of satellite-to-vehicle link and the terrestrial BS-to-vehicle link are formulated, respectively. To solve such two problems, we propose two corresponding beamforming optimization approaches, where semi-definite relaxation (SDR) and semi-definite programming (SDP) are adopted due to the non-convexity. In addition, the tightness of SDR is proved and the complexity of proposed approaches is also analyzed. Finally, extensive numerical simulations are carried out and results show the effectiveness of our proposed approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".