To Jam or Not to Jam in Gaussian MIMO Wiretap Channels ?: Invited Paper
Bibliographic record
Abstract
The popular technique of secure signaling over Gaussian MIMO wiretap channels, which makes use of artificial noise (AN) to increase secrecy rates, is considered. First, we briefly review the current state of affairs in this area and then provide new analytical results and insights on the usefulness of AN (jamming) to boost secrecy rates. The settings considered here go beyond the total transmit power constraint and include a number of additional constraints, such as interference (cognitive radio) and energy-harvesting constraints, for which the feasible set is not isotropic anymore so that the standard tools of the analysis cannot be used. By closely examining optimal precoding for the information-bearing and AN signals, we identify a number of cases where it is optimal to transmit no artificial noise at all (so that all the transmit power goes to the information-bearing signal). These cases include a fixed (no fading) MIMO WTC with single eavesdropper (for which we give a direct matrix-theoretic proof using novel matrix inequalities), multi-eavesdropper (com-pound) degraded and reversely-degraded channels, and multi-eavesdropper channels where there exists a dominant eavesdropper (for which we give a precise definition) or when the eavesdroppers collude. To improve secrecy rates by using AN, one has to look elsewhere.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".