Secrecy Rate Analysis of Satellite Communications With Frequency Domain NOMA
Bibliographic record
Abstract
Due to the inherent broadcasting nature and broad coverage of satellite, satellite communications are well known to be vulnerable to security threats. Since the distance difference from satellite to terrestrial terminals is negligible, the channels of different users are similar, posing a challenge of achieving secure satellite downlink transmission in the presence of eavesdroppers (Eves). In this paper, we consider satellite communications in areas without terrestrial networks converge, and investigate the physical layer security in the satellite downlink. To achieve a positive secrecy rate, a frequency domain non-orthogonal multiple access (FD-NOMA) scheme and an according multiuser cooperative scheme are proposed. Particularly, by adopting the FD-NOMA, the spectrum efficiency can be improved at the cost of raising inter-user interference (IUI), and the inherent IUI is elegantly leveraged to suppress the signal-to-interference-plus-noise ratio (SINR) of Eves while the intended SINR of legitimate users can be enhanced by the cooperative scheme. The secrecy rate of satellite communications with FD-NOMA is analyzed, and a tight lower bound is derived, which is validated via numerical results. In addition, the secrecy rate is found to be affected by the level of spectral overlapping, and there exists an optimal spectral overlapping factor (SOF) which can maximize the secrecy rate.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".