A Model for Correlated Rician Fading Channels Based on a Finite Queue
Bibliographic record
Abstract
We study the problem of approximating the family of hard-decision frequency-shift keying demodulated correlated flat Rician fading channels via a recently introduced queue-based channel (QBC) model for binary communication channels with memory. For a given ldquodiscretizedrdquo fading channel, we construct a QBC whose noise process is statistically ldquocloserdquo in the Kullback-Leibler sense to the error or noise process that is generated by the fading channel, and the modeling accuracy is evaluated in terms of noise autocorrelation function (ACF) and channel capacity. Numerical results indicate that the QBC provides a good approximation of the fading channels for a wide range of channel conditions. Furthermore, it estimates the noise ACF more accurately than the finite-state Markov models that have been recently studied by Pimentel , while, at the same time, remaining mathematically tractable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".