Exploiting transmitter I/Q imbalance for estimating the number of active users
Bibliographic record
Abstract
The number of active users in a network is crucial for understanding the security level of wireless operating environments, since any node in a network could perform malicious attacks and be a potential threat. In this paper, we propose a novel estimation technique for the number of active users by exploiting a typical device fingerprint - I/Q imbalance, which has been identified as a device-specific hardware impairment and can be utilized to distinguish different wireless devices. In the design, I/Q imbalance of a transmitter is first estimated from its transmitting signals. The estimate is then compared with the observed I/Q imbalances of previously identified users through a hypothesis testing, where the distances between the new estimate and previous observations are adopted as the test metric. If all the distances are larger than a properly selected threshold, a new active user is claimed. Finally, the number of active users is determined by counting all the distinct I/Q imbalances. Simulation results are provided to validate the proposed estimation scheme.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".