<ul><li>Antenna arrays are widely used in wireless communication, radar systems, radio astronomy, and military defense to enhance signal strength, directivity, and interference suppression.</li><li>A deep learning-based optimization approach is introduced to enhance the design of sparse phased arrays by reducing grating lobes.</li><li>Neural networks are used to approximate the non-convex cost function, allowing for cost function minimization through gradient descent.</li><li>The method demonstrates significant cost reductions, ranging from 411% to 643%, with an average improvement of 552% in ten array configurations.</li></ul>

Sparse Phased Array Optimization Using Deep Learning

Discover more