Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms

TL;DR

This paper introduces a deep learning-based method for designing leaky-wave holographic antennas that generate orbital angular momentum, improving performance and precision over traditional tuning methods.

Contribution

It combines flat optics and machine learning to optimize holographic antenna parameters for better radiation patterns, a novel integration in OAM generator design.

Findings

ML determines optimal impedance parameters efficiently

Achieves lower side lobe levels and deeper nulls

Outperforms manual tuning and conventional methods

Abstract

In this paper, we present a novel approach for the design of leaky-wave holographic antennas that generates OAM-carrying electromagnetic waves by combining Flat Optics (FO) and machine learning (ML) techniques. To improve the performance of our system, we use a machine learning technique to discover a mathematical function that can effectively control the entire radiation pattern, i.e., decrease the side lobe level (SLL) while simultaneously increasing the central null depth of the radiation pattern. Precise tuning of the parameters of the impedance equation based on holographic theory is necessary to achieve optimal results in a variety of scenarios. In this research, we applied machine learning to determine the approximate values of the parameters. We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77,000 generated datasets. Furthermore, the use of ML not only saves time, but also yields more precise and accurate results than manual parameter tuning and conventional optimization methods.