Adaptive Spectrum Anti-Jamming in UAV-Enabled Air-to-Ground Networks: A Bimatrix Stackelberg Game Approach
Bibliographic record
Abstract
Anti-jamming communication technology is one of the most critical technologies for establishing secure and reliable communication between unmanned aerial vehicles (UAVs) and ground units. The current research on anti-jamming technology focuses primarily on the power and spatial domains and does not target the issue of intelligent jammer attacks on communication channels. We propose a game-theoretical center frequency selection method for UAV-enabled air-to-ground (A2G) networks to address this challenge. Specifically, we model the central frequency selection problem as a Stackelberg game between the UAV and the jammer, where the UAV is the leader and the jammer is the follower. We develop a formal matrix structure for characterizing the payoff of the UAV and the jammer and theoretically prove that the mixed Nash equilibrium of such a bimatrix Stackelberg game is equivalent to the optimal solution of a linear programming model. Then, we propose an efficient game algorithm via linear programming. Building on this foundation, we champion an efficacious algorithm, underpinned by our novel linear programming solution paradigm, ensuring computational feasibility with polynomial time complexity. Simulation experiments show that our game-theoretical approach can achieve Nash equilibrium and outperform traditional schemes, including the Frequency-Hopping Spread Spectrum (FHSS) and the Random Selection (RS) schemes, in terms of higher payoff and better stability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".