Relay authentication by exploiting I/Q imbalance in amplify-and-forward system
Bibliographic record
Abstract
Although cooperative relaying has been widely utilized in wireless communications, it simultaneously introduces a new source of security vulnerabilities to the wireless networks such as denial of service attacks. In order to minimize the potential security risks from relays, reliable relay authentication schemes become necessitated. In this paper, a novel relay authentication scheme is proposed to secure amplify-and-forward relay systems through utilizing the device-dependent hardware imperfection in-phase/quadrature (I/Q) imbalance. In this scheme, the I/Q imbalance associated with the receiving and transmission of the relaying process is considered as a unique device fingerprint. This fingerprint is then utilized to develop a two-parameter hypothesis testing based authentication. To enhance the performance in differentiating delicate difference between I/Q imbalances, the generalized likelihood ratio test for classical linear model is used in our hypothesis decision algorithm. The performance of the proposed authentication scheme is assessed and validated by numerical simulations. The results show significantly enhanced authentication accuracy of our new method in comparison with other I/Q imbalance based hypothesis decision algorithms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".