A Multi-Domain Anti-Jamming Defense Scheme in Heterogeneous Wireless Networks
Bibliographic record
Abstract
In this paper, we investigate the anti-jamming problem in heterogeneous wireless networks. Although there are many studies on the anti-jamming defense problem in both power domain and spectrum domain, these two important aspects were addressed separately. In this paper, to cope with the jamming attacks flexibly, we study the anti-jamming defense problem from a multi-domain perspective, which includes both power domain and spectrum domain, and a multi-domain anti-jamming scheme (MDAS) is proposed. To be more specific, a Stackelberg power game is formulated in the power domain to fight against the jamming attacks, and a multi-armed bandit-based channel selection with a channel switching cost and unknown channel availability state information is formulated in the spectrum domain. Besides, we analyze the performance of the formulated Stackelberg power game and derive the optimal power strategy and utility of a legitimate user. In addition, it is proved that the proposed anti-jamming scheme has a logarithmic regret. Finally, extensive simulations are conducted to validate the performance of the proposed MDAS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".