QoS-Aware Secrecy Rate Maximization in Untrusted NOMA With Trusted Relay
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Bibliographic record
Abstract
This letter considers a trusted decode-and-forward relay-assisted two-user non-orthogonal multiple access (NOMA) communication system, and tackles the secure rate maximization problem at the near user. The problem is solved optimally using KKT conditions, while satisfying the quality of service (QoS) requirements of the far user. Particularly, closed-form expressions for the power sharing between the source and the relay, and the power allocation coefficients corresponding to power-domain NOMA for both users are obtained. An upper-bound on the threshold rate of the far user is also derived, which indicates the best QoS that can be ensured. Numerical results showed that the relay-user link acts as the bottleneck and provide design insights on optimal power allocation satisfying the tradeoff between the rate secrecy at the near user and the QoS requirements of the far user.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it