Machine Learning-Based Beamforming in K-User MISO Interference Channels

IEEE Access

卷:9

Hyung Jun Kwon¹  Jung Hoon Lee¹  Wan Choi² 

[1] Department of Electronics Engineering and Applied Communications Research Center, Hankuk University of Foreign Studies, Yongin, South Korea;

[2] Institute of New Media and Communications and Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea;

关键词: Machine learning;    MISO interference channels;    beamforming;    deep neural network;    maximum ratio transmission;    zero-forcing;   

DOI  :  10.1109/ACCESS.2021.3058759

来源: DOAJ

【 摘 要 】

In this paper, we consider a K-user multiple-input and single-output interference channel and propose a machine learning-based beamforming design. To circumvent the difficulties of beamforming design in interference channels, we consider a beamforming scheme that combines two well-known beamforming schemes, which are maximum ratio transmission and zero-forcing, with one of finite combining factors. However, this problem is still NP-hard, which requires brute-force search for optimal beamforming, so we adopt the machine learning to find the optimal beamforming vectors. Our machine learning design exploits a deep neural network structure, whose input nodes take channel vectors with transmit power, while the output nodes return the combining factors for the transmitters' beamforming. Our numerical results show that our proposed machine learning-based beamforming achieves a sum rate more than 99% of that with the best combining factors numerically found with brute-force search.

【 授权许可】

Unknown