Two-Stage Beamforming for Rejecting Interferences Using Deep Neural Networks

Abstract

Established beamforming techniques for finding the optimal weight vectors of an antenna array, such as minimum mean squared error (MMSE) and minimum variance distortion-less response (MVDR), often require the direction of arrival (DoA) of the desired signal. In this article, we introduce a neural beamformer to estimate the desired signal in the presence of noise and interferences, without requiring the DoA. The proposed neural beamformer consists of a convolutional neural network (CNN) to estimate the interference vector from signals received at a subarray of the antennas and a bidirectional long short-term memory (bi-LSTM) to estimate the samples of the desired signal. The output signal- to- interference- and noise ratio (SINR) with the proposed neural beamformer is 10dB higher than the conventional beamformers when the number of available snapshots of the received signal is as low as 100. The beamformer can estimate the desired signal when input interference-to-signal ratio (ISR) is as high as 35dB and input signal-to noise ratio (SNR) is as low as -10 dB.