Two-Way Gaussian Networks With a Jammer and Decentralized Control
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Bibliographic record
Abstract
We consider the existence and structure of (zero-sum game) Nash equilibria for a two-way network in the presence of an intelligent jammer capable of tapping the channel signals in both directions. We assume that the source and channel noise signals are all Gaussian random variables, where the source signals are independent of each other, while the noise signals are arbitrarily correlated. We show that for fixed jammer power constraints, a Nash equilibrium exists with respect to the system wide mean square error, and equilibrium jamming policies are always Gaussian. We derive the equilibrium policies in closed form under various system parameters. Finally for two system scenarios, we analytically determine the optimal power allocation levels the jammer can deploy in each channel link, when allowed to operate under an overall power constraint.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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