Reducing Correlation in Compact Arrays by Adjusting Near-Field Phase Distribution for MIMO Applications
Bibliographic record
Abstract
This paper presents a novel method of improving the correlation performance in a compact multiple-input multiple-output (MIMO) array. Unlike the previous work, the proposed method focuses on each antenna phase distribution in the array and reduces the correlation by adjusting the near-field phase distribution using phase correcting elements (PCEs). The PCEs provide the required transmission phase to modify the near-field phase distribution in a specific plane. Diversity measure and channel capacity are introduced as metrics to evaluate the MIMO system's array performances. The significance of the phase pattern on the correlation performance is investigated and demonstrated by comparing and analyzing four specially designed arrays' results. A 1 × 4 dual-polarized patch array with four PCEs is simulated, fabricated, and measured to verify the method. The diversity measure and channel capacity are improved by 0.5 (10%), 5 bit/s/Hz (12.2%), respectively, at 2.6 GHz when the channel's angular spread is 90°.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".