A Hierarchical Learning Solution for Anti-Jamming Stackelberg Game With Discrete Power Strategies
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Bibliographic record
Abstract
This letter investigates the anti-jamming problem with discrete power strategies, and then a Stackelberg game is formulated to model the competitive interactions between the user and jammer. Specifically, the user acts as the leader, whereas the jammer is the follower. Based on their own utilities, the user and jammer select their power strategies and determine their respective optimal strategies. Also, a hierarchical power control algorithm (HPCA) is proposed to obtain the Stackelberg equilibrium, and the asymptotic convergence is analyzed. In addition, we consider the impact of the imperfect information due to the jammer's bounded rationality and inaccurate observation of the user's action. Finally, simulations are conducted to show the effectiveness of the proposed HPCA algorithm, and simulation results demonstrate that the jammer's bounded rationality and limited observation lead to the increase of the user's utility.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 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