Deep Learning Assisted Automatic Methodology for Implanted MIMO Antenna Designs on Large Ground Plane

Lida Kouhalvandi,Ladislau Matekovits,Ildiko Peter

doi:10.3390/electronics11010047

Abstract

This paper provides a novel methodology for designing implanted multiple-input and multiple-output (MIMO) antennas in the automatic fashion. The proposed optimization consists of two sequential phases for firstly configuring the geometry of an implanted MIMO antenna and then sizing the design parameters through the hierarchy top-down optimization (TDO) and regression deep neural network (DNN), respectively. It tackles the difficulty in constructing the structure of antennas and also provides optimal values for the determined variables, sufficiently. This methodology results in valid electromagnetic (EM)-verified post-layout generation that is ready-to-fabricate. The effectiveness of the proposed optimization-oriented method is verified by designing and optimizing the implanted MIMO antenna in the frequency band of 4.34–4.61 GHz and 5.86–6.64 GHz suitable for medical applications at the emerging wireless band. For our design, we employ the actual biological tissues as bone, liquid (%1 sodium chloride, %40 sugar in distilled water), and plexiglass surroundings with a bio-compatible substrate, as aluminium oxide on a large ground plane, that is suitable to be used in a particular biomedical applications involving smart implants.

Highlights

IntroductionFuture wireless medical technologies are expected to explosively grow for controlling the bodily functions and to gather the data of different physiological parameters [1]
Publisher’s Note: MDPI stays neutralFuture wireless medical technologies are expected to explosively grow for controlling the bodily functions and to gather the data of different physiological parameters [1]
This paper presents an automatic methodology for designing and optimizing implanted MIMO antennas; to the best of authors’ knowledge, the proposed method is for the very first time reported in the literature

Summary

Introduction

Future wireless medical technologies are expected to explosively grow for controlling the bodily functions and to gather the data of different physiological parameters [1]. Multiple-input, multiple-output (MIMO) antennas are commonly used in the wireless communication systems due to the advanced capability in supporting data even under interference situations [4,5]. Designing MIMO antennas that can be used for medical communication services can be a very good solution to increase the accuracy of the system. Due to the complexity of MIMO antennas, it is not straightforward to model and design these circuits, and optimization-based approaches are importantly required. Reported various optimization methods around radio frequency and antenna designs are particle swarm optimization [6,7], ant colony optimization [8,9], chicken swarm optimization [10], harmony search algorithm [11], and genetic algorithm [12]; when the design with regard to jurisdictional claims in published maps and institutional affiliations

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Conclusion