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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".