Pixel-Metastructure-Assisted Subarray Decoupling for Compact MIMO Antenna Designs
Bibliographic record
Abstract
This paper presents a method for constructing compact multiple-input multiple-output (MIMO) antennas based on subarray partition and decoupling. An efficient two-step approach is proposed to transform theN-dimensional scattering matrix into a zero matrix. First, the intra-subarray coupling of two co-located-fed antenna elements is counteracted using a section of coupled transmission line. Second, the topology-optimized pixel metastructures, with flexibly controllable network response, are introduced between subarrays for inter-subarray decoupling. By partitioning the high-dimensional scattering matrix into sub-blocks describing intra- and inter-subarray couplings, the multi-element array decoupling problem can be decomposed into smaller-scale sub-problems and solved step by step. To verify the proposed design method, a compact 4-element planar MIMO array with an area of merely 0.603λL× 0.414λL(at the lowest frequency of the 15-dB isolation band) is prototyped and experimentally characterized, which offers a 15-dB isolation bandwidth of 11.0% and total efficiency up to 92%. Finally, extension of the design to a six-port MIMO array is demonstrated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".