Reliability enhancement for CIR-based physical layer authentication
Bibliographic record
Abstract
The inherent properties of channel impulse response CIR, which are considered as location-specific characteristics of the physical link, have been exploited for the authentication purpose at the physical layer in the wireless communications. Unfortunately, the reliability of CIR-based physical layer authentication is challenged by the noise present in the CIR estimates, the rapid channel variation induced by the mobility of terminals, and the weak authentication decision by exploiting single CIR difference under the hypothesis testing. In this paper, three CIR-based authentication schemes are proposed to enhance the authentication reliability. Specifically, the noise components of the CIR estimates are mitigated in order to derive an adaptive threshold to form the authentication decision. Additionally, because of the rapid variation of the fading channel, channel prediction technique is employed to predict future CIR, and which is exploited to derive the CIR difference for the authentication analysis. Furthermore, to form the final decision in the authentication process, multiple CIR differences are observed by the receiver in a long range based on the channel predictor. In order to optimize the number of CIR differences, an optimization algorithm is developed by minimizing the total error rate under a false alarm constraint. Finally, the false alarm rate and the probability of detection are theoretically derived for performance evaluation, and the performance of proposed schemes is compared with that of a traditional channel-based authentication method using computer simulation. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".