Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models

Abstract

Owing to the variations in subject-specific body morphology and anatomy, the radiation performance of a wireless device in the presence of human body is different across subjects. To quantify the inter-subject variations, a large number of highly realistic human models are required. One recent approach is the fast development of body models directly from medical images with machine learning. In this study, a total of eighteen anatomical head models were developed using a fast machine learning approach and were then adopted for large-scale evaluation of the inter-subject variations in antenna performance. The antenna impedance, return loss (RL), total radiated power (TRP), directivity, radiation patterns, and specific absorption rate (SAR) were investigated. The results show rather large variations in impedance, RL, and SAR across subjects, while TRP, directivity, and radiation pattern are less likely to be affected by internal tissue distributions when compared with homogeneous models.

Keywords

• Antenna performance
• electromagnetic exposure
• FDTD method
• machine learning