Physical layer authentication in OFDM systems based on hypothesis testing of CFO estimates
Bibliographic record
Abstract
Information security is becoming a critical challenge in wireless communications due to the open nature of wireless channels and the transparency of standardized transmission schemes. Among the various wireless security techniques, user authentication is one essential measure to identify legitimate users and protect the integrity of transmissions. In this paper, a novel physical layer authentication scheme is proposed to enhance the communication security by exploiting the unique characteristics of oscillator in each communication device. In realistic scenarios, radio frequency (RF) oscillators in each transmitter and receiver pair always present some bias to the nominal carrier frequency due to manufacturing limitations and operating conditions. This bias is characterized by a device-dependent carrier frequency offset (CFO), which can be used to identify a specific wireless transmitter. In the proposed authentication scheme, the CFO at different time of the received signal is first estimated. It is then examined by a hypothesis testing to determine whether the signal has the consistent CFO for authentication purpose. Adaptive thresholds of CFO variation are derived for user discrimination based on the received signal-to-noise ratio (SNR). Simulation results further confirm the effectiveness of the proposed scheme in multipath fading environments.
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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 | 0.007 | 0.044 |
| 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 | 0.002 |
| 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".