Efficient Spatio-Temporal Predictive Learning for Massive MIMO CSI Prediction  

作　　者：CHENG Jiaming CHEN Wei LI Lun AI Bo 

机构地区：[1]School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China [2]State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen 518055,China [3]ZTE Corporation,Shenzhen 518057,China

出　　处：《ZTE Communications》2025年第1期3-10,共8页中兴通讯技术(英文版)

基　　金：supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001;ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。

摘　　要：Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.

关 键 词：massive MIMO deep learning CSI prediction CSI feedback 

分 类 号：TN9[电子电信—信息与通信工程]