Performance of pcs with antenna diversity in sub-Rayleigh fading
Bibliographic record
Abstract
Nakagami fading channels have been used as a very flexible, and fairly accurate approximation of realistic fading in wireless systems [1-3]. In particular, sub-Rayleigh fading, i.e. fading with severity parameter 0.5 ≤ m ≤ 1, describes situation where only a few major scatterers contribute to the signal in the antenna of the receiver, and there is no Line of Sight (LOS) present. Due to a finite spatial correlation of the incident electromagnetic field, and the movement of the vehicle, the fading process possesses certain correlation properties [13], which must be accurately represented when numerical simulation of wireless systems is considered. There are very few algorithms which allow such accurate modelling [3-5]. Most of these algorithm are either based on some numerical evaluation of the parameters of the model [4], or provide an approximation for the resulting correlation function with a fixed uncontrollable error [5]. As a result these models have limiting application in laborious Monte Carlo simulation of the wireless systems. In this paper we suggest a model which allows complete analytical description of the marginal probability density function (PDF) of the envelope and its correlation function. At the same time it provides for an accurate numerical simulation algorithm along with the possibility to derive probability density of any order. In contrast to [4], we provide analytically tractable algorithms which allows the model parameters to be found in closed form. At the same time we avoid the complications related to the representation of a correlation function as a product of two correlation functions [4]. As an example we consider application of the simulation technique suggested to a system with antenna diversity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".