Partial-NOMA Based PLS: Following NOMA Decoding Principle or Enhanced Decoding Design?
Bibliographic record
Abstract
In the insecure transmission environment, compared with the passive eavesdropper (Eve), the active Eve plays a more threatening role in the physical layer security (PLS), since it can wiretap and jam the signals. To simultaneously decrease the wiretap massage for Eve and improve secrecy outage probability (SOP) for the legitimate users, an enhanced decoding protocol in PLS is proposed for the device-to-device (D2D) non-orthogonal multiple access (NOMA) system under an active Eve attack. In addition, the partial NOMA (P-NOMA) is firstly introduced into PLS to further enhance the performance of the proposed system. The provided closed-forms of SOPs are verified via simulations, whose results show the superiority of our proposed schemes over the conventional NOMA schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".