Machine Learning-Aided Design of Dielectric-Filled Slotted Waveguide Antennas With Specified Sidelobe Levels

Abstract

This paper presents the use of machine learning (ML) to facilitate the design of dielectric-filled Slotted Waveguide Antennas (SWAs) with specified sidelobe levels. Conventional design methods for air-filled SWAs require the simultaneous solving of complex equations to deduce the antenna's design parameters, which typically requires further manual simulation-based optimization to reach the desired resonance frequency and sidelobe level ratio (SLR). The few works that investigated the design of filled SWAs, did not optimize the design for a specified SLR. For an accelerated design process in the case of specified SLRs, we formulate the design of dielectric-filled SWAs as a regression problem where based on input specifications of the antenna's SLR, reflection coefficient, frequency of operation, and relative permittivity of the dielectric material, the developed ML model predicts the filled SWA's design parameters fast and with very low error. These parameters include the unified slots length and the non-uniform slots displacements required to achieve the desired performance. We experiment with several regressive ML algorithms and provide a comparative study of their results. Our numerical evaluations and validation experiments with the best performing ML models demonstrate the high efficiency of the proposed ML approach in estimating the dielectric-filled SWA's design parameters in only a few milliseconds. A comparison to the design obtained through conventional optimization using the Genetic Algorithm also indicate superiority in computation time and resulting antenna performance.

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“…SVR is motivated by the ubiquitous Support vector Machines (SVMs) classifier and is the most frequently opted type of support vector machine in regression analysis. Optimal hyperplane calculation is the basic idea in Support Vector Machines (SVM) to find an that best separates the data into two classes and then use this hyperplane to determine the regression function [12]. SVR is characterized by the use of margin control and the kernel.…”

“…All the operations were performed on Intel Core i7-7700 3.6 GHz processor with 8 GB RAM. Moreover, The GPR, RF, SVR, and DT ML models were developed and trained using the Scikit Library whereas the ANN model for this work was developed using the Tensor flow library and trained using ADAM algorithm [12]- [16].…”

“…Different regression-based ML models are used to predict design parameters of Slotted Waveguide Antenna for the desired frequency of operation and specified sidelobe level ratios (SLR) in [12]. In [17], the effect of design parameters affected by epistemic uncertainty on the productivity of textile-based antenna is reported.…”