Joint Energy and Correlation Based Anti-Intercepts for Ground Combat Vehicles
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".