A cross-correlation model for non-isotropic scattering with non-omnidirectional antennas in MIMO propagation channels
Bibliographic record
Abstract
We present a cross-correlation model for multiple-input multiple-output (MIMO) Rayleigh fading channels in a two-dimensional (2D) multipath random media when energy is non-uniformly received/transmitted to/from the receiver/transmitter along propagation directions. We investigate the impact of non-omnidirectional propagation pattern of antennas along with the impact of non-uniform distribution of the scatterers in the propagation environment which introduces non-isotropic wave propagation, at both transmitter and receiver ends. The non-isotropic propagation is described by non-uniform probability density functions (pdf) for the direction-of-departure (DOD) and the direction-of-arrival (DOA) of the outgoing/incoming propagating waves from/to stations. The propagation patterns of antenna elements (and the effect of mutual coupling between them) are also described by the Fourier series expansion of antenna propagation patterns. The expression of the cross-correlation function (CCF) turns out to be a linear expansion of a number of Bessel functions of the first kind. The coefficients of this expansion are given by linear convolution of the Fourier series coefficients (FSC) of the corresponding antenna patterns and the FSCs of the corresponding pdf of the non-isotropic propagation directions. The Fourier analysis on the CCF shows impacts of non-isotropic environment and non-omnidirectional antennas on the spectrum of the received channel process while the maximum Doppler frequency shift remains invariant with variations of beam-patterns and the pdf of propagating waves.
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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) | 0.002 | 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 | 0.002 | 0.002 |
| 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".