Sparse Phased Array Optimization Using Deep Learning Article Swipe

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David Lu , Lior Maman , James P. Earls , Amir Boag , Pierre Baldi ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2504.17073

Antenna arrays are widely used in wireless communication, radar systems, radio astronomy, and military defense to enhance signal strength, directivity, and interference suppression. We introduce a deep learning-based optimization approach that enhances the design of sparse phased arrays by reducing grating lobes. This approach begins by generating sparse array configurations to address the non-convex challenges and extensive degrees of freedom inherent in array design. We use neural networks to approximate the non-convex cost function that estimates the energy ratio between the main and side lobes. This differentiable approximation facilitates cost function minimization through gradient descent, optimizing the antenna elements' coordinates and leading to an improved layout. Additionally, we incorporate a tailored penalty mechanism that includes various physical and design constraints into the optimization process, enhancing its robustness and practical applicability. We demonstrate the effectiveness of our method by applying it to the ten array configurations with the lowest initial costs, achieving further cost reductions ranging from 411% to 643%, with an impressive average improvement of 552%. By significantly reducing side lobe levels in antenna arrays, this breakthrough paves the way for ultra-precise beamforming, enhanced interference mitigation, and next-generation wireless and radar systems with unprecedented efficiency and clarity.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2504.17073
PDF: https://arxiv.org/pdf/2504.17073
OA Status: green
OpenAlex ID: https://openalex.org/W4415306808

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OpenAlex ID: https://openalex.org/W4415306808

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DOI: https://doi.org/10.48550/arxiv.2504.17073

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Title: Sparse Phased Array Optimization Using Deep Learning

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-04-23

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Authors: David Lu, Lior Maman, James P. Earls, Amir Boag, Pierre Baldi

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Landing page: https://arxiv.org/abs/2504.17073

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PDF URL: https://arxiv.org/pdf/2504.17073

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OA status: green

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OA URL: https://arxiv.org/pdf/2504.17073

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Cited by: 0

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