Multipath Canceled RF Fingerprinting for Wireless OFDM Devices Based on Hammerstein System Parameter Separation
Bibliographic record
Abstract
Radio frequency fingerprint (RFF) based authentication of wireless devices can be used in the fields of the access security at the physical-layer of wireless networks and radio spectrum management. However, the stability of RFF is easily damaged by the wireless multipath fading channel in mobile communications. An RFF fingerprinting method with the nonlinearity and in-phase and quadrature (IQ) imbalance of the transmitter is proposed for chunk-based wireless orthogonal frequency division multiplexing (OFDM) devices based on a Hammerstein system parameter separation technique, which cancels the adverse influence of the time-varying multipath channel. First, the parameters of the nonlinear model of the transmitter and finite impulse response (FIR) of the wireless multipath channel are estimated with the Hammerstein system parameter separation technique. Second, the best IQ imbalance parameter combination is obtained with the FIR estimation of the channel. Finally, the estimated parameters of the nonlinear model and IQ imbalance are used as RFFs to classify the transmitters. Theoretical analyses and numerical experiments demonstrate that the obtained RFFs are stable and the fusion authentication of transmitters with subtle differences from the same model and same series is feasible using the novel method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".