Privacy preserving with adaptive link selection for hybrid radio-frequency and free space optical networks
Bibliographic record
Abstract
In order to improve the secrecy performance, we propose a secure hybrid radio frequency/free space optical (RF/FSO) transmission scheme that takes advantage of both the RF and FSO channels to protect the privacy messages. In the proposed scheme, Alice adaptively selects RF link or FSO link for information transmission according to the secrecy performance of each link. Considering the priority of the FSO link, we propose two secure transmission policies: the FSO dominant secure (FDS) policy and the secrecy rate optimal (SRO) policy. In the FDS policy, we assign high priority to the FSO link due to its high secrecy performance. Therefore, in this policy, Alice transmits the privacy information through the FSO link when the FSO link is reliable. When the FSO link cannot provide successful transmission, Alice will consider the RF secure transmission. In the SRO policy, Alice optimally selects FSO link or RF link according to the secrecy rates of both the FSO and RF links in each time slot. For both FDS and SRO policies, we analyze the secrecy performances and derive closed-form expressions for the average secrecy rate. Numerical results demonstrate the performance improvement of the proposed policies when compared with the current RF or FSO secure schemes in terms of the average 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".