multi-stage beamforming using dnns

Through beamforming, the desired signal is estimated by calculating the weighted sum of the input signals of an array of antenna elements. in the classical beamforming methods, computing the optimal weight vector requires prior knowledge on the direction of arrival (doa) of the desired signal sources. however, in practice, the doa of the signal of interest is unknown. in this paper, we introduce two different deep-neuralnetwork- based beamformers which can estimate the signal of interest while suppressing noise and interferences in two/three stages when the doas are unknown. employing deep neural networks (dnns) such as convolutional neural networks (cnns) and bidirectional long short-term memory (bi-lstm) networks enables the proposed method to have better performance than existing methods. in most cases, the output signal to interference and noise ratio (sinr) of the proposed beamformer is more than 10db higher than the output sinr of the classical beamformers.