A 10-Ports MIMO Antenna System for 5G Smart-Phone Applications
Bibliographic record
Abstract
This paper presents a 10-ports, hybrid multiple-input multiple-output (MIMO) antenna system for 5G Smartphone applications. The proposed antenna system comprises two types of antenna modules: (1) multi-band module consists of two identical multiband antenna elements, each antenna element in this module covers the 2G bands (GSM 850/900/1800/1900 MHz), 3G band (UMTS 2100 MHz) and 4G bands (LTE 2300/2500), and (2) single-band module consists of eight identical L shape elements, each antenna element in this module covers the C-band (3400-3600 MHz) for 5G mobile application. The overall dimensions of the proposed antenna system are 150 × 80 mm2. The proposed antenna system is fabricated and tested. Experimental results show reflection coefficients better than -6 dB and -10 dB for multi-band and single-band modules, respectively, with high isolation levels between the antenna elements in both modules. Moreover, the measured envelop correlation coefficients (ECC) is well below 0.3 and 0.1 for the proposed multi-band and single-bands modules, respectively. In addition, single antenna elements in both modules show good radiation characteristics with maximum peak gain between 2 dBi and 4 dBi. Finally, 43 bps/Hz channel capacity is achieved in the single-band module. With these characteristics, the proposed antenna system can be a good candidate in the modern mobile communication systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".