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

• URL: http://arxiv.org/abs/2304.12695v1
• Date: Tue, 25 Apr 2023 10:01:04 GMT
• Title: Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms
• Authors: Naser Omrani, Fardin Ghorbani, Sina Beyraghi, Homayoon Oraizi, Hossein Soleimani
• Abstract summary: 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. We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77,000 generated datasets.
• Score: 0.6999740786886535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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