Secrecy-Enhancing Design for Cooperative Downlink and Uplink NOMA With an Untrusted Relay
Bibliographic record
Abstract
We investigate a secrecy-enhancing design for cooperative downlink and uplink non-orthogonal multiple access (NOMA) transmissions with an untrusted relay. A source uses the NOMA principle to have downlink and uplink transmissions with a near user and a far user, and the communications between the source and the far user are aided by an untrusted relay. To minimize information leakage at the untrusted relay and achieve secure NOMA communications, adaptive downlink and uplink cooperative jamming schemes are proposed, where the far user in downlink and the near user in uplink adaptively emit a jamming signal to the untrusted relay to impair its eavesdropping capability. Both downlink and uplink jamming power are optimized to fully exploit the benefits of the proposed schemes for security enhancement. Then, for each scheme, we quantify the secrecy performance by deriving the ergodic secrecy sum rate lower bound and its scaling law. Finally, computer simulation is used to demonstrate the effectiveness of the proposed adaptive downlink and uplink cooperative jamming schemes and verify the accuracy of the derived analytical results.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".