Artificial-Noise Alignment for Secure Multicast using Multiple Antennas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We propose an artificial-noise alignment scheme for multicasting a common-confidential message to a group of legitimate receivers. Our scheme transmits a superposition of information and noise symbols. At each legitimate receiver, the noise symbols are aligned in such a way that the information symbols can be decoded with high probability. In contrast, the noise symbols completely mask the information symbols at the eavesdroppers. Our proposed scheme does not use the knowledge of the eavesdropper's channel gains at the transmitter for alignment, yet it achieves the best-known lower bound on the secure degrees of freedom. The knowledge of the eavesdropper's channel gains is still necessary when selecting the rate of the wiretap code. Our scheme is also a natural generalization of the approach of transmitting artificial noise in the null-space of the legitimate receiver's channel, previously proposed in the literature.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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