Deep learning for joint channel estimation and feedback in massive MIMO systems  

作　　者：Jiajia Guo Tong Chen Shi Jin Geoffrey Ye Li Xin Wang Xiaolin Hou 

机构地区：[1]National Mobile Communications Research Laboratory,Southeast University,Nanjing,210096,China [2]Department of Electrical and Electronic Engineering,Imperial College London,London,SW72AZ,UK [3]DOCOMO Beijing Communications Laboratories Co.,Ltd,Beijing,China

出　　处：《Digital Communications and Networks》2024年第1期83-93,共11页数字通信与网络（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(NSFC)under Grants 61941104,61921004;the Key Research and Development Program of Shandong Province under Grant 2020CXGC010108;the Southeast University-China Mobile Research Institute Joint Innovation Center;supported in part by the Scientific Research Foundation of Graduate School of Southeast University under Grant YBPY2118.

摘　　要：The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

关 键 词：Channel estimation CSI feedback Deep learning Massive MIMO FDD 

分 类 号：TN929.5[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程]