Location-Aware Anti-Jamming Game: An Intelligent Approach for Reliable Wireless Communications
Bibliographic record
Abstract
The innovative development of wireless communication technologies has witnessed a growing number of dynamic jamming attacks. Enabling the receiver to make real-time decisions that enhance the communication quality from the transmitter and to defend against the interference from the jammer becomes a key challenge, which can be modeled as a game. In such a game, the receiver aims to optimize communication performance by adjusting its geographical location and negotiating frequency band. Meanwhile, the jammer tries to launch more effective attacks according to decisions made by the receiver. Nevertheless, the complex electromagnetic environment, combined with the vast decision possibilities thereby induced, poses significant challenges in accurately quantifying payoff matrices within such games. Specifically, two-dimensional geographical location and one-dimensional frequency band form a three-dimensional strategy space, further introducing difficulties for intelligent and efficient decision-making. To address these challenges, we first develop a location-aware anti-jamming game (LAJ game) to characterize the simultaneous decision-making and non-cooperative relationship between the receiver and the jammer, with Nash Equilibrium (NE) being the optimal strategy for both game players. Through appropriate discretization, we then compute two payoff matrices based on various geographical locations and frequency bands. Finally, we propose ResBiNet, a continuous series-parallel architecture that utilizes inverted residual blocks to construct a deep cascade residual network, specifically designed to effectively solve large-scale LAJ games. Extensive experiments using real-world and synthesized datasets demonstrate that our proposed method can exponentially reduce running time while achieving an approximation error of less than 5%, offering an intelligent and efficient solution to address location-aware anti-jamming communication challenges.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".