Secrecy Capacity Scaling With Untrustworthy Aggressive Relays Cooperating With a Wire-Tapper
Bibliographic record
Abstract
In this letter, we investigate the performance of the secrecy capacity in an amplify-and-forward dual-hop network for both distributed beamforming (DBF) and opportunistic relaying (OR) techniques. We derive the capacity scaling for two sets; U untrustworthy aggressive relays cooperating together with a wire-tapper to intercept the message, and T trustworthy relays, for a large number of nodes. We prove that the capacity scaling in the DBF is bounded by a value depending on a ratio between the number of the trustworthy and the untrustworthy aggressive relays. Finally, we show that DBF is better than OR whose capacity scaling is proved to be upper-bounded by a value tending to zero when the untrustworthy relays are aggressive. Simulation results confirm our analytical derivations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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 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".