Bidirectional position attention lightweight network for massive MIMO CSI feedback  

作　　者：Li Jun Wang Yukai Zhang Zhichen He Bo Zheng Wenjing Lin Fei 

机构地区：[1]School of Information and Automation,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China [2]School of Information Science and Engineering,Shandong University,Qingdao 266237,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2024年第5期1-11,共11页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(12005108);the Shandong Provincial Natural Science Foundation Youth Project(ZR2020QF016);the National Natural Science Foundation of China(U2006222)。

摘　　要：In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information(CSI)feedback methods.Specifically,a bidirectional position attention module(BPAM)was designed in the BPANet to improve the network performance.The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information,thereby enhancing the feature representation of the CSI matrix.Furthermore,channel attention is decomposed into two one-dimensional(1D)feature encoding processes effectively reducing computational costs.Simulation results demonstrate that,compared with the existing representative method complex input lightweight neural network(CLNet),BPANet reduces computational complexity by an average of 19.4%and improves accuracy by an average of 7.1%.Additionally,it performs better in terms of running time delay and cosine similarity.

关 键 词：massive multiple-input multiple-output(MIMO) channel state information(CSI)feedback deep learning lightweight neural network bidirectional position attention module(BPAM) 

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]