Disguised jamming against OFDM transmission through nonlinear amplify-and-forward
Bibliographic record
Abstract
Disguised jamming can be used to disrupt adversary wireless transmission without being noticed. In this paper, a novel nonlinear amplify-and-forward (NAF) jamming scheme is proposed to perform disguised jamming on wireless OFDM transmission, by exploiting the vulnerability of OFDM systems to nonlinear distortion. In the proposed design, the jammer nonlinearly amplifies captured signals and then forwards them to the receiver of the target communication system. Consequently, constellation rotation of the target communication signals, coupled with inter-carrier interference and additional noise, is imposed to the receiver. The OFDM transmission is thus disrupted. As the jamming signals are just a distorted version of the communication signals, which can also be induced by hardware imperfection of the communication system itself, the jamming attack is difficult to detect. Moreover, the proposed jamming scheme requires no information about the communication system and no synchronization to target signals. In addition, it can be easily implemented by analog circuits without any digital signal processing. Simulation results are provided to validate the proposed NAF jamming scheme.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".