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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Huang, Hai Liu, Ruxin Yang, Jinwei Suo, Luyao Du, Ruiyan Northeastern University at Qinhuangdao Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

In the case of interference perturbation, the wideband adaptive beamforming (WAB) weight vector may be mismatched, which leads to the decrease of interference suppression ability. To improve communication quality under the interference position perturbation, this paper presents a multi-head self-attention conventional neural network (MHSA-CNN)-based WAB algorithm with null broadening. In the presented approach, a MHSA-CNN structure is proposed to improve the prediction accuracy of beamforming weight vector in the case of interference perturbation. Specifically, by processing multiple attention heads in parallel to obtain the information of different signal subspaces, MHSA mechanism enables the network to dynamically adjust the attention distribution of signal features and effectively extract the global features of the covariance matrix. Then, based on focused reconstruction and null broadening, an effective neural network training label is used to enhance the ability of suppressing interferences. Finally, the well-trained MHSA-CNN can accurately output the weight vector suitable for WAB with null broadening in real time. Simulation results demonstrate that the proposed algorithm can suppress interferences accurately within the interference perturbation range and enhance the output signal-to-interference-plus-noise ratio while ensuring real-time communication performance. © 2025 IEEE.

关键词： Signal to noise ratio

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Efficient Wideband Beamforming with Null Broadening Using Fusion Convolutional Block

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Li, Chenglong Feng, Liwen Gao, Yajie Shi, Baozhu Du, Ruiyan Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing 066004 China Northeastern University School of Computer Science and Engineering Shenyang 110819 China Hebei University College of Mathematics and Information Science Baoding 071002 China

To meet the development requirements of bandwidth and efficiency in future 6G wireless communications, an efficient wideband beamforming (WB) algorithm is proposed based on fusion convolutional block neural network (FCBNN) in this paper, named as WB-FCBNN algorithm. The presented algorithm can significantly enhance the real-time performance while obtaining a superior output signal to interference plus noise ratio (SINR) of WB. Firstly, the algorithm utilizes null broadening algorithm with interference plus noise covariance matrix (INCM) reconstruction to generate labels, avoiding the influence of interference direction of arrival (DOA) estimation errors. Then, a novel FCBNN framework is constructed to enhance the real-time performance and the output SINR. Specifically, a fusion convolutional block (FCB) is introduced into the FCBNN framework, which can fuse the asymmetric convolution and the standard convolution into one convolution. In this way, the network parameters will be reduced, thus the computational complexity of the network can be reduced. Furthermore, the FCBNN model can be trained via the label. Finally, the well-trained FCBNN model can quickly predict the near-optimal weight vector of WB. Simulation results demonstrate that the presented algorithm can effectively increase the real-time performance of WB, and still has a satisfactory SINR in the environment of high SNR and low snapshot. © 2017 IEEE.

关键词： Array signal processing deep learning wideband beamforming

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