Physical-Layer Authentication Enhancement via Random Watermark Hopping
Bibliographic record
Abstract
Existing physical-layer authentication (PLA) schemes of tag superimposed on message signals (TSM) can achieve high authentication accuracy at the cost of increased latency and reduced communication performance. The schemes of tag superimposed on pilot signals (TSP) achieve desirable communication performance and low latency, but low randomness of the tag results in lower security. To further improve both security and communication performance, we propose a pseudo random watermark hopping-based PLA scheme in this article. The proposed scheme generates a pseudo-random sequence and designs a watermark hopping mechanism, which superimposes a carefully designed tag on the pilot or message signals accordingly. The proposed scheme enhances the security by utilizing the randomness from both tag generation and watermark hopping mechanism. Meanwhile, it decreases the authentication latency and improves the communication performance by superimposing the tag on the pilot signals without the message recovery process before authentication. The theoretical and experimental results demonstrate that the proposed scheme decreases the bit error rate (BER) and outage probability as well as increases the achievable rate of the system compared with the TSM scheme with the same key equivocation. Moreover, the security performance of our scheme is significantly improved compared with both TSM and TSP schemes.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".