Deep INCM Reconstruction for Adaptive Beamforming

Abstract

The interference-plus-noise covariance matrix (INCM) reconstruction-based adaptive beamforming methods have been successful in preventing signal self-nulling. However, their computational complexity is generally high, which cannot be neglected. In this paper, we propose a data-driven adaptive beamforming method named Deep-Reconstruction, which utilizes deep learning to establish a direct mapping from the sample covariance matrix to the inverse of the INCM. Specifically, we devise a Unet-based fully convolutional network to extract the low-dimensional representations of interferences and noise from the sample covariance matrix. Meanwhile, a conjugate symmetrization layer is designed to maintain a Hermitian structure of the network output. As a result, an accurate estimation of the inverse of the INCM can be obtained for the beamformer design. Simulation results demonstrate that the proposed method can effectively avoid signal self-nulling, while achieving a higher computational efficiency as compared to the traditional methods.