A 3D correlation model for MIMO non-isotropic scattering with arbitrary antenna arrays
Bibliographic record
Abstract
We introduce a multiple-input multiple-output (MIMO) space-time-frequency wireless channel model for the wave propagation in three-dimensional (3D) space. The cross-correlation function (CCF) between two subchannels of the MIMO communication system is decomposed into some non-negative functions. These functions are expressed in terms of a selection of channel parameters, such as the carrier frequencies, the delay profile, the path-loss exponent, the softness factor, /spl theta/, the non-uniform distribution of directions of arrival and departure, array geometries, and the mobile speed. We introduce a class of distributions for the elevation angle (EA) spreads as a basis such that any arbitrary (isotropic or non-isotropic) EA distribution can be represented by a convex linear combination of this class. The corresponding term of the CCF of the 3D-MIMO model for any EA distribution equals to the same linear combination of the basic CCF terms associated to the class. This 3D-MIMO model formulates the CCF as a function the spacial separation of antennas, time, and carrier frequencies in terms of physical channel parameters such as mobile speed, delay profile and distribution of scavengers around mobile and base stations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".