Near-field Characterization of Large-Scale mm-wave Massive MIMO Arrays using IDM Computed EM Lagrangian Density
Bibliographic record
Abstract
In this paper, use of IDM (infinitesimal dipole model) computed EM Lagrangian density is proposed to characterize the NF (near-field) of large-scale mm-wave MMIMO (massive MIMO) arrays for 6 G applications. As full-wave EM simulation is infeasible for such large-scale MMIMO arrays, the proposed IDM-approach offers an useful time and memoryefficient route for NF analysis, with better accuracy compared to conventional isotropic element based technique. The normalized Poynting vector magnitude and EM Lagrangian density in the NF region are visualized for large-scale ID-based URAs. It is shown that the spatial distribution of normalized EM Lagrangian density plots provide an alternative paradigm as compared to the traditional phase-error criteria to quantify the “Fresnel distance” (boundary between the reactive and radiating NF region) for large-scale MMIMO arrays. The impact of the array-aperture dimension in configuring the distribution of NF reactive energy density is further highlighted through the large-scale ID-URA examples.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".