Dual Anti-Jamming Alleviation for Radio Frequency/Free-Space Optical (RF/FSO) Tactical Systems
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".