Joint Intelligent Reflecting Surface-Aided Frequency-Hopping Anti-Jamming for Tactical Wireless Systems
Bibliographic record
Abstract
The frequency hopping (FH) technique has always been crucial for anti-jamming tactical applications thanks to its advantages in avoiding the jammer's interception. However, modern tactical scenarios require FH systems to not only undertake defence missions but also meet increasingly high Quality of service (QoS) requirements. Unlike the prior works that mainly optimize FH systems by balancing anti-jamming capability and QoS performance, we propose a collaboration of FH and the intelligent reflecting surface (IRS) in an advanced anti-jamming scheme. Such approach shares the burden with the IRS and improves QoS. We formulate a joint IRS-aided FH anti-jamming problem as a Mixed Integer Programming (MIP) non-convex optimization. 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). The numerical results show that the performance of our solution is close to optimal, and it is scalable to be applicable in practical situations.
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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".