Deep unfolding model-based for hybrid robust wide band adaptive beamforming

Abstract

The design of arrays capable of receiving wideband signals differs from arrays that can only receive narrowband signals. These arrays must be able to receive signals with an instant bandwidth of several GHz across the entire operating frequency, such as High-Resolution Radars or Terahertz in 6G communication systems. In these arrays, using a time delay line structure leads to an increase in beamformer coefficients, resulting in high computational complexity. This poses a challenge for beamforming in wideband systems. Additionally, classic Wideband beamformers face other factors, such as poor performance in the presence of input direction of arrival error, array calibration error, and requiring too many snapshots to reach the steady state of the beamformer. Therefore, the robustness of wideband adaptive beamforming using deep unfolding model-based technique is focused on, which has not been discussed before. The advent of deep unfolding, an innovative technique, amalgamates iterative optimization approaches with elements of neural networks. The aim is to deftly maneuver through various tasks across disciplines such as machine learning, signal and image processing, and telecommunication systems. Also, the network training method is done to become more robust against the mentioned factors. In the proposed structure, the constraints of the previous methods have been evaluated. It is observed to have better performance compared to other classic algorithms. Also, with the investigations of the proposed method with other conventional deep learning methods, it was observed that in some cases the proposed structure performance is equal to the conventional deep learning method and sometimes better.