Unsupervised Learning-Based Fast Beamforming Design for Downlink MIMO

IEEE Access

卷:7

Xiaomei Zhu¹  Wenchao Xia²  Jie Yang³  Jian Xiong³  Hao Huang³  Gan Zheng⁴ 

[1] College of Computer Science and Technology, Nanjing Tech University, Nanjing, China;

[2] Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, China;

[3] Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, China;

[4] Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, U.K.;

关键词: MIMO;    beamforming;    deep learning;    unsupervised learning;    network pruning;   

DOI  :  10.1109/ACCESS.2018.2887308

来源: DOAJ

【 摘 要 】

In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input–multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the “APoZ”-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, the experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.

【 授权许可】

Unknown