南京邮电大学学报（自然科学版）2024，Vol.44Issue(3)：1-7,7.DOI:10.14132/j.cnki.1673-5439.2024.03.001
基于特征融合的大规模MIMO系统CSI反馈
CSI feedback for large-scale MIMO systems based on feature fusion
摘要
Abstract
Channel state information(CSI)feedback is a key issue in large-scale multiple-input multiple-output(MIMO)systems.The number of base station antennas in large-scale MIMO systems is huge,and the CSI feedback holds problems such as large feedback overhead and low feedback accuracy.In regard of these,a feature fusion-based CSI feedback network,FFNet,is proposed based on a deep learning approach.The CSI features are fused at different scales in the encoder,while an attention-based mechanism of feature fusion,a multi-channel multi-resolution convolutional network,and the channel rearrangement are deployed in the decoder.Thus,the compressed CSI is reconstructed with high accuracy.Simulation results show that the feedback accuracy is higher in both indoor and outdoor channel conditions,compared to several classical deep learning CSI feedback methods.关键词
大规模MIMO/信道状态信息/深度学习/卷积神经网络/特征融合Key words
large-scale multiple-input multiple-output(MIMO)/channel state information(CSI)/deep learning/convolutional neural network/feature fusion分类
电子信息工程引用本文复制引用
安永丽,蔡浩然,胡泽冰,纪占林..基于特征融合的大规模MIMO系统CSI反馈[J].南京邮电大学学报（自然科学版）,2024,44(3):1-7,7.基金项目
国家科技部重点研发专项(2017YFE0135700)和河北省高层次人才工程项目(A201903011)资助项目 （2017YFE0135700）
