Joint Energy and Correlation Based Anti-Intercepts for Ground Combat Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
Today, ground combat vehicles (GCVs) in Warfighter Information Network-Tactical (WIN-T) systems are highly interconnected and autonomous. However, protecting a large number of wireless communication links against the interception of enemy in a dynamic environment is challenging. Because of GCV mobility, the Low Probability of Intercept (LPI) capacity is easily violated, in particular when multiple interception techniques are used simultaneously. In this paper, we investigate the problem of preserving LPI capability under traditional optimization and Deep Reinforcement Learning (DRL) approaches. Unlike prior work, we propose an anti-interception strategy against both energy-based and correlation-based interceptors techniques. Our strategy jointly optimizes power allocation (PA) and spreading factor assignment (SA) of the WIN-T to avoid these interceptors. The problem is mathematically formulated as a non-convex optimization model, and therefore we solve it by advanced techniques such as decomposition and difference of convex functions (DC). To obtain the optimized solution in near real-time, we design a Multi-Agent Deep Reinforcement Learning (MADRL) strategy. Our numerical results show the performance of the proposed MADRL strategy is close to the optimal solution, making it applicable for the practical systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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