Antenna parameter effects on spatial channel models
Bibliographic record
Abstract
The comparison of the outage capacity for multiple-input multiple-output (MIMO) channel models based on different underlying approaches is made. Three different channel models are considered: the 3GPP empirical spatial channel model (SCM), a multi-element transmit and receive antenna (METRA) analytical spatial channel model (A-SCM) and the correlation-based long-term evolution (LTE) channel model. The authors evaluate the models' predicted channel capacity for different antenna element separation, array orientation and angle spread, with and without mutual coupling. The authors compare these results with measurement campaigns from the literature. The authors also derive an effective distance term that combines the antenna element separation, array orientation and angle spread parameters. The authors use this value to describe the effect on the signal correlation of the antenna output, and thereby explain the outage capacity dependence on the variables. Among the considered channels, the SCM showed the best agreement with the measurement literature, followed by the A-SCM and then the LTE model. The SCM was also the most computationally involved, followed by the A-SCM and then the LTE model. Our analysis showed that the mutual coupling had a small impact on the performance of all channel models, especially for antenna element separations greater than half a wavelength.
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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 | 0.002 | 0.009 |
| 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) | 0.001 | 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".