Combination of Pareto Optimal Front with Deep Neural Networks in Optimizing and Enhancing Performance of RF Designs

Abstract

This paper focuses on the implementation of Pareto optimal front (POF) with deep neural networks (DNNs) for optimizing and integrating of radio frequency (RF) designs. POF typically generates the set of optimal trade-offs while the DNNs employ the automated environment for estimating the targeted specifications. Hence combination of these two tools provides a strong optimization environment for the high-dimensional RF designs. This work illustrates the importance of this combination and explains the general procedure for optimizing RF active devices (such as amplifiers) and RF passive devices (such as antennas). The optimization method that is based on the POF idea is selected as the Thompson sampling efficient multiobjective optimization (TSEMO) algorithm and the DNN is elected to be constructed by the long short term memory (LSTM) layers. Here two separate configurations are considered: the optimized amplifier is operating in the frequency band from 1.7 GHz to 2.2 GHz. Additionally, the antenna design is operated in the frequency band from 3.1 GHz to 10.6 GHz for proving the importance of POF method where DNN is employed to enhance the overall performance of the RF designs.