典型文献
A Unified Deep Learning Method for CSI Feedback in Massive MIMO Systems
文献摘要：
A unified deep learning (DL) based algorithm is proposed for channel state information (CSI) compression in massive multiple-input multiple-output (MIMO) systems. More importantly, the element filling strategy is investigated to address the problem of model redesign?ing and retraining for different antenna typologies in practical systems. The results show that the proposed DL-based algorithm achieves better performance than the enhanced Type Ⅱ algorithm in Release 16 of 3GPP. The proposed element filling strategy enables one-time training of a unified model to compress and reconstruct different channel state matrices in a practical MIMO system.
文献关键词：
中图分类号：
作者姓名：
GAO Zhengguang;LI Lun;WU Hao;TU Xuezhen;HAN Bingtao
作者机构：
State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen 518055,China;The College of Computer Science and Technology,Nanjing Univer?sity of Aeronautics and Astronautics,Nanjing 210016,China
文献出处：
引用格式：
[1]GAO Zhengguang;LI Lun;WU Hao;TU Xuezhen;HAN Bingtao-.A Unified Deep Learning Method for CSI Feedback in Massive MIMO Systems)[J].中兴通讯技术（英文版）,2022(04):110-115
A类：
B类：
Unified,Deep,Learning,Method,CSI,Feedback,Massive,MIMO,Systems,unified,deep,learning,DL,algorithm,is,proposed,channel,state,information,compression,massive,multiple,input,output,systems,More,importantly,element,filling,strategy,investigated,address,problem,model,redesign,retraining,different,antenna,typologies,practical,results,show,that,achieves,better,performance,than,enhanced,Type,Release,3GPP,enables,one,reconstruct,matrices
AB值：
0.6276
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