Depolarization of Propagating Signals by Narrowband Ricean Fading Channels
Bibliographic record
Abstract
In many cases of practical interest, the angle of arrival distribution at the receiver is sufficiently narrow that one can use knowledge of the mean received signal levels, Ricean K-factors and the cross-correlation coefficient that characterize fading signals observed on orthogonally polarized diversity branches to predict the of polarization state dispersion. This allows one to use simple power-only measurements of narrowband polarization diversity to predict the performance of alternative polarization diversity schemes or polarization adaptive antennas in realistic environments. Moreover, our results offer a useful geometric interpretation of how decorrelation between polarization diversity branches arises: As the angular spread of the polarization state distribution on the Poincare sphere broadens, the correlation between branches decreases. However, knowledge of the angular spread alone is not sufficient to predict the cross-correlation coefficient; the Ricean K-factors on the branches must be known as well. Otherwise, the analogy to a similar relationship between the angle of arrival distribution and correlation between branches in space diversity is striking.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".