Four-Port MIMO Antenna System for 5G n79 Band RF Devices

In this article, a compact four-port MIMO antenna system resonating from 4.7–5.1 GHz on −6 dB criteria is discussed. The proposed antennas are arranged in a perpendicular manner providing diversity with good isolation characteristics. The proposed antenna was fabricated and designed on a commercially available low-cost FR-4 substrate with a relative permittivity of 4.4. The total size of the antenna is 40 × 40 mm2, and a minimum isolation of 25 dB was observed at most nearby resonating elements. The proposed antenna was fabricated and tested at an in-house facility, and the measured results agree well with the simulations. The MIMO antenna characteristics, such as the envelope correlation coefficient (ECC) among any two radiating elements, have been found to be less than 0.1, and the diversity gain (DG) value evaluated showed that the proposed antenna is well designed. Furthermore, the SAR analysis showed that the desired antenna system is safe for users, with a value of 0.94 W/Kg. The channel capacity (cc) was found to be 18.7 bps/Hz, approximately 2.7 times more than SISO systems. Through its robust and reliable performance and its peak gain of 2.8 dBi, the proposed compact antenna is a good candidate for future 5G devices.

A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model

Recently, carbon nanotube field-effect transistors (CNTFETs) have attracted wide attention as promising candidates for components in the next generation of electronic devices. In particular CNTFET-based RF devices and circuits show superior performance to those built with silicon FETs since they are able to obtain higher power-gain and cut-off frequency at lower power dissipation. The aim of this paper is to present a compact, design-oriented model of CNTFETs that is able to ease the development of a complete amplifier. As a case study, the detailed design of a high-gain CNTFET-based broadband inductorless LNA is presented.

Design of a compact reconfigurable bandpass filter using interdigital capacitor, DMS slots and varactor diode for wireless RF systems

This paper proposes a new design of a reconfigurable bandpass filter based on an interdigital capacitor and varactor diode for wireless and mobile applications. The designed reconfigurable bandpass filter has been implemented on an RT 6010 substrate with a relative dielectric constant of 10.2, thickness of 1.27 mm, and loss tangent of 0.0023. In order to reduce the filter size, the defected microstrip structure (DMS) is used due to its easy design, high compactness, high quality factor and easy integration with other RF devices. The suggested reconfigurable filter has a simple structure with a very attractive compact size of 4.7 × 8.4 mm2, low insertion loss than -1 dB, and tuning range (2–2.6 GHz).

Modeling Radio-Frequency Devices Based on Deep Learning Technique

An advanced method of modeling radio-frequency (RF) devices based on a deep learning technique is proposed for accurate prediction of S parameters. The S parameters of RF devices calculated by full-wave electromagnetic solvers along with the metallic geometry of the structure, permittivity and thickness of the dielectric layers of the RF devices are used partly as the training and partly as testing data for the deep learning structure. To implement the training procedure efficiently, a novel selection method of training data considering critical points is introduced. In order to rapidly and accurately map the geometrical parameters of the RF devices to the S parameters, deep neural networks are used to establish the multiple non-linear transforms. The hidden-layers of the neural networks are adaptively chosen based on the frequency response of the RF devices to guarantee the accuracy of generated model. The Adam optimization algorithm is utilized for the acceleration of training. With the established deep learning model of a parameterized device, the S parameters can efficiently be obtained when the device geometrical parameters change. Comparing with the traditional modeling method that uses shallow neural networks, the proposed method can achieve better accuracy, especially when the training data are non-uniform. Three RF devices, including a rectangular inductor, an interdigital capacitor, and two coupled transmission lines, are used for building and verifying the deep neural network. It is shown that the deep neural network has good robustness and excellent generalization ability. Even for very wide frequency band (0–100 GHz), the maximum relative error of the coupled transmission lines using the proposed method is below 3%.