Exploiting transmitter I/Q imbalance for estimating the number of active users
Why this work is in the frame
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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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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 it