Improved estimation of the ricean K-factor from I/Q fading channel samples
Why this work is in the frame
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Bibliographic record
Abstract
The Ricean K-factor is a practical channel quality measure in many wireless communication applications as it exhibits a fast estimation convergence compared with the explicit estimation of performance metrics such as error rates. Recently, it has been shown that estimation of the K-factor can be improved relative to envelope-based detectors through the use of complex (I/Q) channel observations. In this paper, an analysis of the maximum likelihood estimator of the K-factor from I/Q samples is presented, which illustrates the bias of previous estimators. An improved estimator is then proposed, which has superior bias and efficiency for short data records. For mobile applications, a reliable estimator of the Doppler shift of the specular component is incorporated. Simulation results for a range of channel conditions illustrate that the proposed estimator outperforms prior techniques, and provide insight into the record lengths required to achieve a desired estimation accuracy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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