Relay Selection to Improve Secrecy in Cooperative Threshold Decode-and-Forward Relaying
Bibliographic record
Abstract
In this paper, relay selection is considered to enhance security of a cooperative system with multiple threshold-selection decode-and-forward (DF) relays. Threshold-selection DF relays are the relays in which a predefined signal-to-noise ratio is set for the condition of successful decoding of the source message. We focus on the practical and general scenario where the channels suffer from independent non-identical Rayleigh fading and where the direct links between the source and destination and source and eavesdropper are available. Based on channel state information knowledge, three relay selection strategies, namely traditional, improved traditional, and optimal, are studied. In particular, the secrecy outage probability of all three strategies are obtained in closed-form. It is found that the diversity of secrecy outage probability of all strategies can improve with increasing the number of relays. It is also observed that the secrecy outage probability is limited by either the source to relay or relay to destination channel quality.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".