Design and Optimization of a Compact Super-Wideband MIMO Antenna with High Isolation and Gain for 5G Applications
Bibliographic record
Abstract
This paper presents a super-wideband multiple-input multiple-output (SWB MIMO) antenna with low profile, low mutual coupling, high gain, and compact size for microwave and millimeter-wave (mm-wave) fifth-generation (5G) applications. A single antenna is a simple elliptical-square shape with a small physical size of 20 × 20 × 0.787 mm3. The combination of both square and elliptical shapes results in an exceptionally broad impedance bandwidth spanning from 3.4 to 70 GHz. Antenna dimensions are optimized using the trust-region algorithm to enhance its impedance bandwidth and maintain the gain within a predefined limit across the entire band. For that purpose, regularized merit function is defined, which permits to control both the single antenna reflection response and gain. Subsequently, the SWB MIMO system is constructed with four radiators arranged orthogonally. This arrangement results in high isolation, better than 20 dB, over a frequency band from 3.4 to 70 GHz band. Further, the system achieves an average gain of approximately 7 dB below 45 GHz and a maximum gain equal to 12 dB for 70 GHz. The system exhibits excellent diversity performance throughout the entire bandwidth, as evidenced by the low envelope correlation coefficient (ECC) (<3 × 10−3), total active reflection coefficient (TARC) (≤−10 dB), and channel capacity loss (CCL) (<0.3 bit/s/Hz) metrics, as well as the high diversity gain (DG) of approximately 10 dB. Experimental validation of the developed SWB MIMO demonstrates a good matching between the measurements and simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".