Comparative Study of Anti-Jamming Beamforming Using Deep Learning in Planar Phased Array Antennas

In this study, a deep learning-based beamforming comparative study for anti-jamming applications in 2D-planar phased arrays is presented. For better array architecture benchmarking, three different geometries (circular, rectangular, hexagonal) are considered. Convolutional Neural Network (CNN) is employed to translate a target radiation pattern, generated as an image, directly into the optimal antenna currents. Adaptive antenna array beamforming weights can be estimated efficiently by the deep learning-based MATLAB code according to the desired beam steering angle and the null direction of the jammer. This approach establishes a smart, non-iterative mapping that bypasses traditional optimization algorithms, reducing computation time by up to 260x. Once trained, the model delivers optimal currents and weights in a single and efficient forward pass.