Distributed-Relay Beamforming for Secrecy Energy Efficiency With Coordinated Eavesdroppers
Why this work is in the frame
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Bibliographic record
Abstract
This letter proposes a secure and energy efficient beamforming scheme for a distributed amplify-and-forward relay network against multiple coordinated eavesdroppers. Specifically, an optimization problem is formulated to maximize the secrecy energy efficiency (SEE), defined as the ratio of secrecy rate (SR) to total power consumption, subject to the target SR and both total and individual relay transmit power constraints. Due to the difficulty in solving this problem arisen from the non-convexity of fractional form of SEE and logarithmic subtraction of SR, we first adopt the Dinkelbach’s method to handle the original problem by iteratively solving a sequence of parametric problems. Then, by jointly applying the penalty function approach and the difference of convex functions programming, we convert the parametric problem into a convex one which can be further approximated as a second order cone programming problem to reduce the computational complexity. Finally, we propose an iterative algorithm to find the near-optimal solution. Numerical results demonstrating the effectiveness of the proposed scheme and its advantage over the conventional approaches are provided.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it