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This paper presents a novel concept for creating a Graphical User Interface (GUI) tool for analyzing the beamforming behavior and sensing the radiation pattern of planar-phased array antennas. It incorporates built-in functionality to specify active elements and allows for importing radiation pattern data from external software. Additionally, the tool generates interactive 3-D plots of the radiation patterns and sensed beams. The main objective of this paper is to provide a comprehensive understanding of the tool's functionality and describe the methodology used to develop the software package for sensing the beam direction. The proposed software offers a user-friendly interface that simplifies the analysis of planarphased array antennas for beam sensing. By leveraging the power of Python, the tool enables efficient sensing of antenna radiation patterns and beamforming. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, a rectangular dielectric resonator (DR) based multiple input multiple output (MIMO) hybrid antenna with a machine learning (ML) optimization approach for 5G New Radio (NR), WiMAX, WLAN, Wi‐Fi 6, Vehicle to Everything (V2X), and C‐band uplink applications are presented. The proposed antenna provides a triple band. It covers n1, n2, and n39 5G NR at a lower frequency (1.59–2.26 GHz), n48 5G NR at the middle frequency (3.1–3.87 GHz), and n46, n47, n96, WiMAX, WLAN, and C‐band uplink at higher frequency (5.25–6.91 GHz). It achieved more than 20% impedance bandwidth and 20 dB isolation in all frequency bands. The maximum gain of the proposed antenna is 15 dBi. The calculated MIMO parameters are within the accepted range. It is optimized through various ML algorithms, including random forest (RF), K‐nearest neighbor (KNN), artificial neural network (ANN), extreme gradient boosting (XGB) and decision tree (DT). The RF ML algorithm gives 98.09% accuracy compared to other ML algorithms for S parameter prediction. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, a compact triple‐band frequency‐tunable (FT) hexagonal‐shaped graphene antenna through a machine learning (ML) approach for terahertz (THz) application is presented. The proposed THz antenna is designed on a polyamide (∈r=3.5$$ {\in}_r=3.5 $$) substrate with a thickness of 10 μm, and graphene is used as an antenna radiator. The size of the substrate is 38 × 46 μm2. The FT is achieved by changing the chemical potential of graphene material. The performance of the proposed THz antenna has been investigated, and the impacts of several conducting materials like gold, aluminum, copper, and graphene and dielectric materials like Rogers RT/duroid 5880, polyamide, quartz, and SiO2 are explored. The proposed THz antenna provides three operating bands. The frequency of operation in Band‐1 is 2.51–5.05 THz, Band‐2 is 5.99–7.43 THz, and Band‐3 is 7.94–9.63 THz. The bandwidth in Band‐1, Band‐2, and Band‐3 are 2.54, 1.44, and 1.69 THz, respectively. The % of impedance bandwidth in Band‐1, Band‐2, and Band‐3 are 67.19%, 24.02%, and 21.28% respectively. The proposed antenna has a maximum peak gain of 5 dBi. The proposed antenna is optimized through various ML algorithms like random forest (RF), extreme gradient boosting (XGB), K‐nearest neighbor (KNN), decision tree (DT), and artificial neural network (ANN). The RF algorithm gives more than 99% accuracy compared to other ML algorithms and accurately predicts the S11 of the proposed antenna. The proposed THz antenna would be suitable for applications related to imaging, medical, sensing, and ultra‐speed short‐distance communication applications in the THz region. [ABSTRACT FROM AUTHOR]

Summary: In this article, a two‐port multiple‐input multiple‐output (MIMO) hybrid rectangular dielectric resonator antenna (DRA) with machine learning (ML) approach for the n261 5G New Radio (NR) application is presented. The proposed antenna is designed on an RT/duroid 5880 (Ɛr = 2.2) substrate activated by 50 Ω, L‐shaped microstrip slot feeds beneath both DRAs. The isolation is more than 19 dB, and the gain is 10 dBi in the operating frequency range. The proposed antenna is optimized through knowledge‐based neural networks (KBNN), artificial neural networks (ANNs), and ML. The optimal design parameters of the proposed antenna are accomplished using the ML optimization approach, which includes ridge regression, ANNs, and KBNN. KBNN ML techniques provide 96.88% accuracy and correctly predict the S‐parameters of the proposed antenna. The MIMO diversity parameters like envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), and channel capacity loss (CCL) are calculated and found within the limits. Hence, the proposed antenna is used for 5G NR mm‐wave application. [ABSTRACT FROM AUTHOR]

Summary In this article, a compact quad element coplanar waveguide (CPW) fed ultra‐wideband (UWB) multiple input multiple output (MIMO) antenna for future generation wireless communication system using a machine learning (ML) optimization approach is presented. The proposed antenna is used for 5G new radio (n46/n77/n47/n78/n48/n79), Wi‐Fi 5, Wi‐Fi 6, and dedicated short range communications (DSRC) services, vehicle to infrastructure (V2I), vehicle to vehicle (V2V), and vehicle to network (V2N) in the entire operating frequency band. It is operating from 3.2 to 11.85 GHz. The bandwidth is 8.65 GHz, and the percentage of impedance bandwidth is 115%. The comparative analysis between dual and quad elements are presented. It is optimized through the various ML model K‐nearest neighbor (KNN), extreme gradient boosting (XGB), artificial neural network (ANN), and random forest (RF). The KNN ML model achieved a higher accuracy of 93%, and it accurately predicted the S parameters of the suggested UWB antenna. The MIMO parameters are calculated and found within the acceptable limits. There is a strong correlation between the simulated and measured results. Hence, the suggested antenna is a suitable candidate for future wireless communication systems. [ABSTRACT FROM AUTHOR]

Summary: Developing wireless communication systems depends on reconfigurable microwave filters (MF) and dielectric resonator antenna (DRA) because the functionality of the various filters and antennas available for a wireless communication system is limited, and reconfigurable DRA and MF are utilized to overcome these limitations. The throughput of a multiantenna system can be achieved with reconfigurable DRA and filter. Various switching strategies and classifications of reconfigurable DRA and MF are presented. The electrical reconfiguration technique is most suitable for developing a reconfigurable DRA and filter among all the switching techniques. In this approach, reconfigurability may be accomplished by utilizing a PIN diode, a varactor diode, and a radio‐frequency microelectromechanical switch (RF‐MEMS). These switches are more reliable, highly efficient, and easily integrated with the microwave circuit. Reconfigurable DRA with machine learning is demonstrated. The application of reconfigurable DRA and filters in cognitive radio (CR) is also demonstrated. Also, the review article focused on the performance parameters, design, and challenges of reconfigurable antenna and MF. [ABSTRACT FROM AUTHOR]

A high-tuning range frequency reconfigurable dual-band compact cylindrical Dielectric Resonator Antenna (DRA) for 5 G Sub 6 GHz applications is presented in the proposed work. The work presented here provides compactness, frequency reconfigurability (FR), and dual-band. FR is obtained through two PIN diode switches, which operate in ON-ON, ON-OFF, OFF-ON, and OFF-OFF configurations and offer a maximum wide tuning range and a total spectrum of 44.44% and 71.49%, respectively. In the cylindrical DRA, the ${\rm{HE}}{{\rm{M}}_{11{\rm{\delta }}}}$HEM11δ and ${\rm{HE}}{{\rm{M}}_{12{\rm{\delta }}}}$HEM12δ modes create dual-band, while the hybrid structure and higher order mode excitation yield compactness. [ABSTRACT FROM AUTHOR]