ArticleScientific reports2025
Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Predicting the performance of a graphene-based patch antenna using a machine learning model for terahertz applications.Scientific reports · 2026Article
- Graphene based terahertz MIMO antenna with machine learning regression for 6G communications.Scientific reports · 2026Article
- Machine learning-inspired design of a modified spider-shaped MIMO antenna for 5G-advanced wireless communications.PloS one · 2026Article
- Article
- Machine learning-optimized compact wearable frequency reconfigurable antenna for sub-6 GHz/mm-wave 5G integration.Scientific reports · 2025Article
- ML/GA-based performance optimization of PBG-enhanced THz microstrip patch antennas on PTFE-SWCNT.Scientific reports · 2025Article
- High-performance quatrefoil-slotted THz MIMO antenna for 6G applications with regression-based machine learning validation.Scientific reports · 2025Article
- Predictive modelling and high-performance enhancement smart thz antennas for 6 g applications using regression machine learning approaches.Scientific reports · 2025Article
- Machine Learning-Driven Inspired MTM and Parasitic Ring Optimization for Enhanced Isolation and Gain in 26 GHz MIMO Antenna Arrays.Micromachines · 2025Article
- High performance grid structured MIMO antenna with regression machine learning for high-speed sub THz and THz 6G IoT applications.Scientific reports · 2025Article
- Machine learning-optimized dual-band wearable antenna for real-time remote patient monitoring in biomedical IoT systems.Scientific reports · 2025Article
- Regression machine learning-based highly efficient dual band MIMO antenna design for mm-Wave 5G application and gain prediction.Scientific reports · 2025Article
- Metamaterial based tri-band compact MIMO antenna system for 5G IoT applications with machine learning performance verification.Scientific reports · 2025Article
- High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction.Scientific reports · 2025Article
- Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications.Scientific reports · 2025Article
- Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents the design and analysis of a compact 28 GHz MIMO antenna for 5G wireless networks, incorporating simulations, measurements, and machine learning (ML) techniques to optimize its performance. With dimensions of 3.19 λ₀ × 3.19 λ₀, the antenna offers a bandwidth of 5.1 GHz, a peak gain of 9.43 dBi, high isolation of 31.37 dB, and an efficiency of 99.6%. Simulations conducted in CST Studio were validated through prototype measurements, showing strong agreement between the measured and simulated results. To further validate the design, an equivalent RLC circuit model was developed and analyzed using ADS, with the reflection coefficient results closely matching those from CST. Additionally, supervised ML techniques were employed to predict the antenna's gain, evaluating nine models using metrics such as R-squared, variance score, mean absolute error, and root mean squared error. Among the models, Random Forest Regression achieved the highest accuracy, delivering approximately 99% reliability in gain prediction. This integration of machine learning with antenna design underscores its potential to optimize performance and enhance design efficiency. With its compact size, high isolation, and exceptional efficiency, the proposed antenna is a promising candidate for 28 GHz 5G applications, offering innovative solutions for next-generation wireless communication.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.