Deep learning based user scheduling for massive MIMO downlink system  

作　　者：Xiaoxiang YU Jiajia GUO Xiao LI Shi JIN 

机构地区：[1]The National Communications Research Laboratory,Southeast University,Nanjing 210096,China

出　　处：《Science China(Information Sciences)》2021年第8期62-71,共10页中国科学（信息科学）（英文版）

基　　金：National Natural Science Foundation of China(Grant Nos.61831013,61971126,61941104,61921004).

摘　　要：In this paper,we investigate a user scheduling algorithm for massive multiple-input multipleoutput(MIMO)systems over more general correlated Rician fading channels.To achieve low latency and high throughput,a new user scheduling algorithm based on deep learning(DL)is proposed,which exploits only statistical channel state information.The proposed scheduling network is trained to grasp the mapping from the statistical signal and interference pattern to the user scheduling decision through supervised learning.It can predict the optimal scheduling scheme from statistical CSI without iterative calculation after offline training.Simulation results demonstrate the superior performance of the proposed algorithm in terms of calculation time,and it achieves almost the same throughput as the optimal scheduling algorithm which is obtained through exhaustive search.Furthermore,with the normalization of the input data,the proposed scheduling network is robust to the change of the channel environment and the number of transmit antennas.

关 键 词：massive MIMO deep learning statistical CSI user scheduling 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]