Sparsity-Aware Channel Estimation for mmWave Massive MIMO: A Deep CNN-Based Approach  

作　　者：Sicong Liu Xiao Huang 

机构地区：[1]Department of Information&Communication Engineering,School of Informatics,Xiamen University,Xiamen 361005,China

出　　处：《China Communications》2021年第6期162-171,共10页中国通信（英文版）

基　　金：This work is supported in part by the National Natural Science Foundation of China under grants 61901403,61971366 and 61971365;in part by the Youth Innovation Fund of Xiamen under grant 3502Z20206039;in part by the Natural Science Foundation of Fujian Province of China under grant 2019J05001.

摘　　要：The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.

关 键 词：deep convolutional neural networks deep learning sparse channel estimation mmWave massive MIMO 

分 类 号：TN929.5[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]