What is the best angular density of multipath in MIMO channels?
Bibliographic record
Abstract
While various models of multipath angular density are used for modeling MIMO fading channels, it is not known which one is the best, i.e. gives the highest channel capacity. We answer this question in two steps. First, we show that the asymptotic capacity of a broad class of MIMO channels (not necessarily Rayleigh-fading) with an arbitrary correlation structure does not depend on particular channel distribution, but only on the correlation between antennas. Second, the best multipath angular density, which eliminates the correlation and thus maximizes the asymptotic capacity of uniform linear antenna arrays, is shown to be non-uniform, which implies that the popular Clarke's model does not represent the best-case scenario. We identify the best angular density for different values of the antenna spacing. The Gaussian angular density (adopted in IEEE 802.11n) is shown to be near optimum when the spacing equals to the wavelength. Based on these results, practical guidelines for the antenna array design are given.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".