Bibliographic record
Abstract
Physical-layer key exchange is a growing area within the intersection of security and wireless communications that leverages communication systems to perform a symmetric key exchange. In this paper we investigate how time diversity can be improved within the coherence time of a wireless channel through the usage of a whitening filter. This process allows for more random key-bits to be generated, when compared to key generation methods that do not utilize our processes. A novel key exchange protocol is proposed that uses an algorithmic estimation of the temporal covariance matrix of the channel to perform the whitening, without the need of manual processing. The base of the protocol is an orthogonal frequency division multiplexing communication system that samples the channel frequency response, which is then used to generate a symmetric key. The protocol is designed to be robust against various channel conditions, including fast, slow, flat, and selective channels, as well as Rayleigh or Rician conditions. To evaluate the protocol, it is simulated to show the Monte-Carlo results of the key mismatch rate between the communicating parties and the randomness results from the NIST standards. These results are compared against other key generation protocols to show the benefits of our proposed methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".