A two dimensional quantization algorithm for CIR-based physical layer authentication
Bibliographic record
Abstract
Recently, channel impulse response (CIR) based physical layer authentication has been studied to enhance the security of wireless communications. However, the reliability of CIR-based authentication is substantially reduced at low signal-to-noise ratio (SNR) conditions due to the presence of communications noise, channel estimation error and mobility induced channel variation. To this end, we integrate additional multipath delay characteristics into the CIR-based physical layer authentication and propose a two dimensional quantization scheme to tolerate these random errors of CIRs for reduced false alarm rate and more reliable spoofing detection. Instead of directly comparing the estimated CIRs from different transmitters for authentication purpose, we first quantize the CIR estimates in two dimensions (i.e., the amplitude dimension and multipath delay dimension) and then differentiate transmitters based on the quantizer outputs with a binary hypothesis testing. More specifically, the quantization intervals are determined by using a searching algorithm based on a guaranteed miss probability of detection of the presence of spoofing attack. A logarithmic likelihood ratio test (LLRT) is used to evaluate the authentication performance, and a threshold with a constant value is used for the decision-making of authentication under the binary hypothesis testing. To verify the effectiveness of proposed algorithm, an orthogonal frequency division multiplexing (OFDM) system is considered in our simulation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".