Design of Dual-Band MIMO Array Antenna and Its Characterization for Urban Millimeter Wave Channel Conditions
Bibliographic record
Abstract
The multiple-input-multiple-output (MIMO) antenna improves the channel capacity, and the array antenna improves the channel condition. A combined feature benefits the current cellular and other 5G applications. The strength of the MIMO array antenna is that it improves channel condition and operates with wide bandwidth and directional beam, achieving high gain. Not many of the articles focused on the second issue. Thus, this article focuses on designing a MIMO array antenna with a directional narrow beam resonating at dual bands that are 28 and 38 GHz. The circular slot in the ground plane serves a dual purpose: (i) improve the bandwidth at both bands ranging from 25.35-30.53 GHz, and 36.76-41.43 GHz. (ii) It acts as a decoupling structure providing isolation |S21| > 39 dB and 42 dB in respective bands. The proposed antenna generates circular (RHCP) and elliptical (LHCP) polarization in the first and second bands. The antenna has achieved half-power beamwidth (HPBW) of 15° and 16.5° in the YZ-plane and a broad beam in the XZ-plane. The antenna is validated for diversity metrics, and the results are satisfactory. The MIMO antenna performance regarding channel capacity and path loss is virtually tested for cellular Picocell urban scenarios with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The computed results agree with the analytical results of standard Friss, Stanford Inter-University (SUI), and Close-In (CI) models.
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 | 0.000 | 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".