A modeling approach for simulating MIMO systems with near-field effects
Bibliographic record
Abstract
A hybrid method of modeling MIMO systems is presented through the combined use of full-wave electromagnetic (EM) simulations and statistical channel models. This approach consists of simulating each antenna in the antenna array to compute its embedded element pattern including all of the near-field electromagnetic and coupling effects. These embedded patterns are then uploaded into the measurement-based statistical spatial channel model (SCM) to study the link-level performance of the MIMO system. The equivalence between the coupling matrix of an antenna array and the embedded element pattern of each antenna is theoretically derived and verified. We compute the channel matrix in two approaches. First, either the theoretically calculated or the simulated embedded radiation patterns are used with SCM to compute the channel matrix. Second, the coupling matrix is used with the default SCM algorithm to compute the channel matrix. Then, the MIMO system capacity is computed and compared. The results show very good agreement between the two approaches. Our approach is general as it includes the detailed near-field EM effects such as the effects of the case and the user on the handset MIMO system.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".