Physical Layer Security in RIS-Assisted THz-Enabled LEO Satellite Communications
Bibliographic record
Abstract
Satellite networks are expected to play a key role in achieving global coverage in the sixth generation (6G) of wireless communications systems. Despite their potential, the operation of extensive satellite constellations introduces distinct challenges, especially in ensuring robust physical-layer security. To address these concerns, emerging strategies involve the deployment of reconfigurable intelligent surfaces (RISs) and the utilization of high-frequency bands like the terahertz (THz) spectrum, both of which offer significant potential for improving the security and efficiency of satellite-based communication systems. This paper examines the secrecy performance of a satellite communication system enhanced by an RIS and operating in the THz band. The system comprises a legitimate link between two satellites, aided by an RIS, and an eavesdropper employing a reflective device to intercept the communication. We provide analytical closed-form approximations for key metrics, including the secrecy outage probability (SOP), the average secrecy rate, the intercept probability, and the probability of non-zero secrecy capacity. Additionally, an asymptotic analysis of the SOP at high signal-to-noise ratios is conducted, providing valuable insights into the system’s behavior. Our results underscore the promise of RIS and THz technologies for achieving robust and secure satellite communications in 6G.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".