Resolution enhancement of microwave sensors using super-resolution generative adversarial network

This article presents an approach to significantly improve the resolution of a highly-sensitive microwave planar sensor response with a super-resolution generative adversarial network (SRGAN). Three identical complementary split-ring resonators are coupled so that the sensitivity is doubled. This highly-sensitive resonator with a deep transmission zero at 4.7 GHz is deployed to measure minute variations of glucose in interstitial fluid. Measuring the sensor response with 1001 frequency-points allows differentiating 10 glucose samples within the range of 40-400 mg/dL. However, in practical readout systems with limited number of frequency-points (here 28), recognizing the deep zero in the S21 response lacks precision. Sensor responses (magnitude vs. frequency and phase vs. frequency) are converted into equivalent 2D images (heatmaps: phase vs. frequency with colored pixels as amplitude) to be compatible as SRGAN input. As a result of 8-fold resolution enhancement using SRGAN, the classification accuracy is substantially improved from 62.1% to 93.3%. The proposed passive sensor followed by an SRGAN unit is shown to be practical as a wearable glucose monitoring sensor due to its high-sensitivity and high resolution features in a low-profile design.