A5099126340- Antenna Design and Analysis
- Advanced MIMO Systems Optimization
- Antenna Design and Optimization
- Microwave Engineering and Waveguides
- Millimeter-Wave Propagation and Modeling
- Wireless Body Area Networks
- Energy Harvesting in Wireless Networks
- Satellite Communication Systems
Daffodil International University
2024-2025
This study presents the design and analysis of a compact 28 GHz MIMO antenna for 5G wireless networks, incorporating simulations, measurements, machine learning (ML) techniques to optimize its performance. With dimensions 3.19 λ₀ × λ₀, offers bandwidth 5.1 GHz, peak gain 9.43 dBi, high isolation 31.37 dB, an efficiency 99.6%. Simulations conducted in CST Studio were validated through prototype showing strong agreement between measured simulated results. To further validate design, equivalent...
This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in terahertz (THz) frequency range. Leveraging meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature to predict properties greater accuracy. Specifically, neural network is applied as base learner predicting parameters, resulting increased...
This paper presents the findings about implementing a machine learning (ML) technique to optimize performance of 5 G mm wave applications utilizing multiple-input multiple-output (MIMO) antennas operating at 28 GHz frequency band. article examines various methodologies, including simulation, measurement, and utilization an RLC-equivalent circuit model, evaluate appropriateness antenna for its intended applications. In addition compact dimensions, proposed design exhibits maximum gain 10.34...
A number of classical machine learning approaches have been used to predict antenna efficiency. However, needs be enhanced more accurately. The stacked generalization approach has shown capable from features and meta features. In this paper, we propose a learner-based ensemble strategy that passes output an optimized multi-feature ensemble. For the optimizer, grid search is employed. Applying ANN model with ML as base learner for predicting efficiency leads increased performance in terms R2,...
This study discusses the results of using a regression machine learning technique to improve performance 6G applications that use multiple-input multiple-output (MIMO) antennas operating at terahertz (THz) frequency band. research evaluates an antenna's various methodologies, such as simulation and RLC equivalent circuit models. The suggested design has broad bandwidth 2.5 THz spans from 6.2 8.7 GHz, maximum gain 14.59 dB, small dimensions (100 × 300) µm