Dual Anti-Jamming Alleviation for Radio Frequency/Free-Space Optical (RF/FSO) Tactical Systems
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Bibliographic record
Abstract
In this paper, we design a jamming alleviation plan to protect a mixed radio frequency/free-space optical (RF/FSO) relay tactical network in the context that both RF and FSO systems are simultaneously attacked by enemy jammers. Unlike prior works that focused mainly on a single type of jamming attack (e.g. RF jamming), our proposed plan can protect the entire network against multiple types of jamming at the same time. We formulate a joint optimization problem of power allocation (PA) and Field-of-View (FoV) tuning strategy to maximize the RF uplink sum rate, subject to capacity and security constraints for both FSO and RF systems. To address this non-convex optimization problem, at first, we derive a closed-form expression of the optimal FoV angle. Then, the optimal FoV angle solution is computed to solve the PA problem. Since the PA problem has a non-convex form, we use an advanced technique of first-order Taylor approximation with the difference of convex functions method to solve it. The obtained solution of the optimization problem is then used for training a machine learning model that optimizes the system in real-time. Based on the Multi-Agent Deep Reinforcement Learning (MADRL) method, we develop a MADRL-based jamming alleviation algorithm to obtain the optimized solution of PA in near real-time. The numerical results show that the performance of the proposed MADRL-based jamming alleviation algorithm with low computational complexity is close to that of the optimization method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it