Physical layer security of interference aligned mixed RF/unified‐FSO relaying network
Bibliographic record
Abstract
In this study, the authors secure the mixed radio frequency/free space optical (FSO) relay‐aided interference aligned system using a proposed physical layer security algorithm. This algorithm reduces the quality of the received signal at the eavesdropper through two procedures. First, it minimises the data transmission power from the legitimate users and the relays. Second, it jams the eavesdropper by broadcasting artificial noise from the users and the relays. Therefore, a joint optimisation problem is formulated to degrade the received signal at the eavesdropper from the users and the relays and to maximise the jamming artificial noise power, which is solved using an iterative optimisation algorithm beside a semi‐definitive programming algorithm. Furthermore, the pre‐coding and decoding matrices of the users and the relay are designed to enable the legitimate users to cancel the artificial noise, while the eavesdropper is disabled from distinguishing the artificial noise from the real streams. Moreover, the security performance of the proposed algorithm is analysed, and the impact of the FSO link's state on the security performance is studied. The extensive simulation results show the efficiency of the proposed algorithm and illustrate the role of the FSO link's state on the security performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".