Jamming Mitigation for Mixed RF/FSO Relay Networks Under Simultaneous Interceptions
Bibliographic record
Abstract
In this paper, we design a jamming mitigation plan to protect a mixed radio frequency/free-space optical (RF/FSO) relay network in the context that both RF and FSO systems are simultaneously attacked by enemy jammers. Our design aims to jointly optimize the power allocation (PA) and Field-of-View (FoV) tuning strategy to maximize the RF uplink sum rate subject to practical constraints on the jamming mitigation in both FSO and RF systems. In order to address the underlying non-convex optimization problem, we first derive the closed-form expression of the optimal Fo V angle. Then, the optimal FoV angle solution is used to solve the optimization PA. Since the PA problem has a non-convex form, we use an advanced technique of first-order Taylor approximation with difference of convex functions (D.C) method to solve it. Moreover, based on the Multi-Agent Deep Reinforcement Learning (MADRL) method, we develop a MADRL-based jamming mitigation 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 mitigation 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 | 0.001 |
| Scholarly communication | 0.001 | 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".