Sparse Phased Array Optimization Using Deep Learning
Journal:
arXiv
Published Date:
Apr 23, 2025
Abstract
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.
