Reliability enhancement for CIR-based physical layer authentication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it