Secrecy Outage Probability of Energy-Harvesting Cooperative NOMA Transmissions With Relay Selection
Bibliographic record
Abstract
Three relay selection techniques, aiming at achieving secure non-orthogonal multiple access in cooperative energy-harvesting (EH) communications, are proposed and compared. In the cooperative relaying system, the source node communicates with multiple users through amplify-and-forward EH relays in the presence of a passive eavesdropper. The relay selection is a two-stage strategy, where the first stage aims at achieving the users' target data rate, and the second aims at optimizing the secrecy outage probability. New explicit analytical expressions for the secrecy outage probability are derived for three operating scenarios: i) when the channel state information (CSI) of the eavesdropper is unknown, and a two-stage conventional relay selection scheme is considered, ii) when CSI of the eavesdropper is known, and a two-stage optimal relay selection scheme is used, and iii) when multiple relays participate in forwarding the signal to the end users. Monte-Carlo simulations are provided to confirm the derivations, and the effects of the main system parameters on its secrecy are investigated. In particular, it is shown that the optimal relay selection scheme outperforms the conventional and the multiple-relays schemes in terms of secrecy outage probability, and that this superiority becomes more obvious when the number of the relays increases.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".