Design of Mimo Antenna Array for Rheinhafen-Dampfkraftwerk Karlsruhe Power Plant 5G Private Network Deployment in N78 Band
Bibliographic record
Abstract
This paper presents the design, simulation, and evaluation of a 4-element bow-tie MIMO antenna array operating in the n78 band 3.3 – 3.8 GHz), targeting industrial 5G applications. The proposed array is derived from a single bow-tie antenna unit cell optimized for broadband operation, ensuring impedance matching and stable radiation characteristics. The MIMO configuration aims to enhance system capacity and minimize mutual coupling, crucial for efficient wireless communication in industrial environments. A comprehensive parametric study was conducted to determine the optimal element spacing, ensuring low correlation and mutual coupling while maintaining high radiation efficiency. The antenna array was simulated using ANSYS HFSS, evaluating key parameters such as$S_{11}$, mutual coupling ($S_{12}$), impedance matching ($Z_{11}$), and radiation patterns. Results indicate that the array maintains a reflection coefficient below −10 dB across the band, with maximum isolation below −20 dB and a peak gain of 4.8 dBi. These findings confirm the suitability of the proposed design for private 5 G networks, particularly in industrial IoT and automation scenarios. Future work will focus on beamforming integration, experimental validation, and deployment in largescale MIMO architectures.
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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) | 0.000 | 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) | 0.002 | 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".