[1]张梦莹,陈璇. 基于压缩感知的大规模MIMO分段信道反馈[J].计算机技术与发展,2017,27(06):183-186.
　ZHANG Meng-ying,CHEN Xuan. Segmental Channel Feedback for Massive MIMO with Compressive Sensing[J].,2017,27(06):183-186.
点击复制

 基于压缩感知的大规模MIMO分段信道反馈()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年06期
页码:
183-186
栏目:
应用开发研究
出版日期:
2017-06-10

文章信息/Info

Title:
 Segmental Channel Feedback for Massive MIMO with Compressive Sensing
文章编号:
1673-629X（2017)06-0183-04
作者:
 张梦莹; 陈璇
 南京邮电大学 通信与信息工程学院
Author(s):
 ZHANG Meng-ying; CHEN Xuan
关键词:
 ; 大规模MIMO; CIR反馈; 压缩感知; 分段CIR
Keywords:
 ; massive MIMO; CIR feedback; compressive sensing; segmental CIRs
分类号:
TP39
文献标志码:
A
摘要:
 大规模多入多出技术(Multiple-Input Multiple-Output,MIMO)是未来5G无线通信的关键技术.在MIMO系统中,发送端的空时编码、接收端的信号检测都需要信道状态信息(Channel State Information,CSI),而大规模MIMO的信道反馈问题随着MIMO信道矩阵的尺寸越来越大,变得越来越具有挑战性.为此,在研究大规模MIMO系统中信道脉冲响应(Channel Impulse Response,CIR)反馈的基础上,提出了一种基于压缩感知的分段CIR反馈方案.应用该方案分段后的信道有着比原信道更好的稀疏性,基站可以利用压缩感知恢复分段后的经过高度压缩的CIR.仿真结果表明,所提出的方案可大幅度降低反馈误差,当压缩率为20%时,直接压缩方案已经失效,而所提出的方案表现却良好;当压缩率为50%时,所提出的方案能够获得高于直接压缩方案5 dB的SNR增益.
Abstract:
 Massive Multiple-Input Multiple-Output (MIMO) is becoming a key technology for future 5G wireless communications.In MIMO systems,Channel State Information (CSI) is essential for both space-time coding at transmitters and signal detection at receivers.Channel feedback for massive MIMO is challenging due to the substantially increased dimension of MIMO channel matrix.For this reason,on the basis of the study of Channel Impulse Response (CIR) feedback for massive MIMO systems,a segmented CIRs feedback scheme based on compressive sensing has been proposed.Specifically,segmented channels are sparser than the original channel.Thus,the base station can recover the highly compressed segmented CIRs under the framework of compressive sensing.Simulation results show that the proposed scheme can reduce the feedback error compared with the direct CS-based scheme and that when compression ratio is 20%,the direct CS-based scheme fails to work since the feedback while the proposed scheme performs well;when compression ratio is 50%,the proposed scheme achieves a 5 dB SNR gain compared with the direct CS-based scheme.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(06):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(06):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(06):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(06):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(06):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(06):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(06):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(06):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(06):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(06):47.
[11]秦舒雅,杨龙祥. 面向5 G的大规模MIMO预编码算法比较研究[J].计算机技术与发展,2015,25(07):150.
　QIN Shu-ya,YANG Long-xiang. Research on Comparison of Pre-coding Algorithm in Massive MIMO for 5 G[J].,2015,25(06):150.
[12]王军[],戴建新[],程崇虎[],等. 基于HPA非线性的大规模MIMO预编码算法[J].计算机技术与发展,2017,27(01):34.
　WANG Jun[],DAI Jian-xin[],CHENG Chong-hu[],et al. Linear Precoding in Massive MIMO Based on Nonlinear High-power Amplifier[J].,2017,27(06):34.
[13]王军[],戴建新[],程崇虎[],等. 一种基于相位噪声的大规模MIMO预编码算法[J].计算机技术与发展,2017,27(03):103.
　WANG Jun[],DAI Jian-xin[],CHENG Chong-hu[],et al. A Linear Pre-coding Algorithm Based on Phase Noise in Massive MIMO[J].,2017,27(06):103.

更新日期/Last Update: 2017-07-28