A cross-correlation MIMO channel model for non-isotropic scattering environment and non-omnidirectional antennas
Bibliographic record
Abstract
We present a cross-correlation model for multiple-input multiple-output (MIMO) Rayleigh fading channels in a two-dimensional (2D) space. In particular, we investigate the impact of non-omnidirectional antennas at both transmitter and receiver ends and the non-uniform distribution of the scatterers in the random media which introduces non-isotropic wave propagation. The non-isotropic propagation is described by general non-uniform probability density functions (pdf) for the direction-of-departure (DOD)/direction-of-arrival (DOA) of the outgoing/incoming propagating waves from/to stations. The propagation pattern of each antenna element and the effect of mutual coupling between them are also described by their Fourier series expansion. The expression of the cross-correlation function (CCF) turns out to be a linear series expansion of a number of Bessel functions of the first kind. In particular the coefficients of the expansion of the CCF are described by linear convolution of the Fourier series coefficients of the antenna pattern, the Fourier series coefficients of the azimuth angular spread and the Fourier series expansion of the pdfs describing the non-isotropic environment. In fact, the Fourier-Chebyshev series expansion of proposed CCF is given in terms of the Fourier series coefficients of the involving distributions and patterns as well as other parameters of the environment
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".