A compact active planar patch antenna array for sub-6 GHz 5G applications
Bibliographic record
Abstract
This paper presents the design, analysis, achievement, and validation of a high-performance 64-element planar patch antenna array tailored explicitly for sub-6 GHz fifth-generation (5G) communication systems. Operating at 3.5 GHz and mounted on an FR4 substrate, the antenna array exhibits a high gain of approximately 17.5 dB, with excellent input impedance matching and radiation efficiency. To further enhance the system's performance, the antenna array was integrated with a custom-designed power amplifier based on the BFP640ESD bipolar transistor. This integration enabled the achievement of an active antenna circuit, improving both transmission power and overall system efficiency. The power amplifier was designed using the Advanced Design System (ADS) solver. To ensure reliable operation, key parameters such as power efficiency, input/output impedance matching, and stability were analyzed and achieved. Integrating the power amplifier, the active antenna array was thoroughly tested into simulation and validated at 3.5 GHz. The results demonstrate that the proposed circuit not only meets the required specifications for 5G applications but also offers enhanced performance in terms of signal strength and matching input impedance across a wide bandwidth. This study confirms the benefit of incorporating an active power amplifier with a planar antenna array for modern wireless 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".