A three-dimensional wideband propagation model for the study of base station antenna arrays with application to LMCS
Bibliographic record
Abstract
A model is proposed herein to simulate wideband correlated diversity channel from the point of view of the base station. It is assumed that scattering activity is limited to a local area around the subscriber and possibly additional secondary scattering areas, each being of local extent. Simulation is possible by generating correlated random variates obeying a complex Gaussian law to represent an instance of the impulse response at each antenna element. A mathematical definition of the correlation existing between discrete channel coefficients is provided as a function of separation in space (lag) and separation in frequency. Thus, the channel correlation between antenna elements is characterized by a lag-frequency correlation function in a three-dimensional (cylinder of scatterers) propagation scenario. The mathematical formulation incorporates the effect of arbitrary antenna patterns at the base and at the subscriber station. Simulation proceeds by dividing the band of interest into a number of frequency bins (each smaller than the coherence bandwidth) leading to the construction of a discrete lag-frequency matrix of channel coefficients. Since the model is not concerned with temporal channel variations, it is appropriate for Monte-Carlo simulations for calculations such as outage probability, system capacity, etc.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".