A Markov chain and quadrature amplitude modulation fading based statistical discrete time model for multi-WSSUS multipath channel
Bibliographic record
Abstract
The computation of the tap gains of the discrete time representation of a slowly time varying multipath channel is investigated. The simplest nondegenerate class of processes which exhibits uncorrelated depressiveness in the time delay and Doppler shifts is known as the "wide sense stationary uncorrelated scattering", (WSSUS) model introduced by Bello (1963). The channel is assumed to be locally WSSUS. Our model presence the quadrature modulation fading simulators (MMFS) form. Assumptions on the multiplicative noise are made to follow Clarke's (1968) model for flat fading. An extension to multipath is provided by utilizing several fading simulators in conjunction with variable gains and time delays. The multipath extended Clarke's model resembles QMFS. However, the multiplicative coefficients are claimed to be Rayleigh distributed (extension to Ricean is easily deduced). The result is a closed form solution for tap gains, this was possible by the use of operator commuting with an error bound of 6f/sub D/T/sup 2/. An extension to large area analysis where WSSUS assumption cannot be in force is made possible through the use of a Markov chain. Finally, further comments and figure results are displayed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".