Countering In-Band Full-Duplex Interception for IRS-aided Frequency Hopping Tactical Networks
Bibliographic record
Abstract
In this paper, we propose an anti-interception scheme to enhance the defence performance of frequency hopping (FH) tactical systems against the in-band full-duplex (IBFD) interception from the enemy. Our scheme utilizes an unmanned aerial vehicle (UAV)-based Intelligent Reflecting Surface (IRS) to share the interception burden with a FH system, thus mitigating jamming effects. To efficiently address IBFD interception, we jointly optimize the control of base station transmit power, FH hopping decision, and IRS phase shift adjustment. This joint optimization is mathematically formulated as a Mixed Integer Programming (MIP) non-convex optimization problem. To address the intractability of traditional optimization methods in solving this problem in modern tactical scenarios, we design a solution based on deep reinforcement learning (DRL). Extensive numerical results show that our proposed scheme significantly enhances the FH anti-interception capability and improves the QoS. Furthermore, the performance of our DRL solution is close to optimal and it is feasible to be deployed in modern practical scenarios.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".