Fully Automated Inside Body WDT Transmitter Design and Optimization Through Artificial Intelligence-Based GANs and DNNs

Abstract

Biomedical inside body wireless data transfer interface includes the design of power amplifiers (PAs) with implantable antenna leading to operate concurrently. Hence, active and passive devices are utilized simultaneously for which the accurate starting points for designing these high dimensional devices is critical. From another point of view, accelerating the design and optimization process is another substantial issue that must be considered effectively. In this study, we propose a methodology that includes two optimization phases that are applied sequentially. In the first phase, the PA is designed and optimized by employing a generative adversarial network (GAN) for predicting the load-pull contours on the Smith chart and using a long short-term memory (LSTM)-based deep neural network (DNN) for achieving the optimal design parameters of the biomedical amplifier. In this step, the GAN leads to predicting the optimal impedances needed to construct the initial structure of PA through a simplified real frequency technique. In the second optimization phase, the initial structure of the biomedical antenna is constructed automatically by developing a visual basic environment, then like the PA, the design parameters of the antenna are optimized through the LSTM-based DNN. Finally, another GAN is generated for predicting the radiation patterns of the antenna. In both phases, a multiobjective ant lion optimizer is employed in the output layer of DNNs for optimizing various outcome specifications. The proposed method is performed fully automatically: active and passive devices are designed and optimized with the help of GANs and DNNs in which the drawback of heavy reliance of the system performance on the designer's experience is solved in a fast way. The proposed method is validated by designing and optimizing a biomedical PA with an antenna working at the center frequency of 2.45 GHz which shows reliable outcomes.