Dual Wireless Anti-Interception for Ground Combat Vehicles
Bibliographic record
Abstract
Protecting a large number of wireless communication links against the interception of enemy in Warfighter Information Network-Tactical (WIN-T) systems is challenging. Due to the high mobility of ground combat vehicles (GCVs), the Low Probability of Intercept (LPI) capacity can easily be violated. Prior work focuses mainly on a single interception technique, which exposes vulnerabilities when multiple interception techniques are deployed simultaneously. We propose a strategy against both energy-based and correlation-based interception techniques by jointly optimizing power allocation (PA) and spreading factor assignment (SA) of the WIN-T. This non-convex problem is then solved by advanced optimization techniques such as decomposition and difference of convex functions (DC). We also propose a communication mode selection strategy to improve the throughput performance in the context of LPI conservation. To obtain the optimized solution in near real-time, we design a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm. Our numerical results show the performance of the proposed MADRL algorithm is close to the optimal solution, making it applicable for practical systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".