Improved Convolutional Neural Network for Wideband Space-Time Beamforming

Abstract

Wideband beamforming technology is an effective solution in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems to compensate for severe path loss through beamforming gain. However, traditional adaptive wideband digital beamforming (AWDBF) algorithms suffer from serious performance degradation when there are insufficient signal snapshots, and the training process of the existing neural network-based wideband beamforming network is slow and unstable. To address the above issues, an AWDBF method based on the convolutional neural network (CNN) structure, the improved wideband beamforming prediction network (IWBPNet), is proposed. The proposed method increases the network’s feature extraction capability for array signals through deep convolutional layers, thus alleviating the problem of insufficient network feature extraction capabilities. In addition, the pooling layers are introduced into the IWBPNet to solve the problem that the fully connected layer of the existing neural network-based wideband beamforming algorithm is too large, resulting in slow network training, and the pooling operation increases the generalization ability of the network. Furthermore, the IWBPNet has good wideband beamforming performance with low signal snapshots, including beam pattern performance and output signal-to-interference-plus-noise ratio (SINR) performance. The simulation results show that the proposed algorithm has superior performance compared with the traditional wideband beamformer with low signal snapshots. Compared with the wideband beamforming algorithm based on the neural network, the training time of IWBPNet is only 10.6% of the original neural network-based wideband beamformer, while the beamforming performance is slightly improved. Simulations and numerical analyses demonstrate the effectiveness and superiority of the proposed wideband beamformer.