Secure DoF for the MIMO MAC: The Case of Knowing Eavesdropper's Channel Statistics Only
Bibliographic record
Abstract
Physical layer security has attracted research attention as a means to achieve secure communication without the need for complicated upper layer encryption techniques. The secure degrees of freedom (SDoF) of various networks in the absence of instantaneous eavesdropper channel state information is still unknown. In this work, we study the SDoF of a multiple access network composed of two transmitters and a single receiver in the presence of an eavesdropper. All parties are equipped with multiple antennas and are subject to Gaussian noise in addition to fading channel conditions. A realistic, worst case scenario, where the channel state information (CSI) for the channels between the trusted parties is known to everyone, while the trusted parties can only estimate the channel statistics (environment based) of the eavesdropper is considered. The asymptotic secure network sum capacity (aka sum SDoF) is provided utilizing a novel proposed comprehensive upperbound along with a novel achievable scheme based on exploiting jamming.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".