Performance Analysis of LDPC Coded Massive MIMO-OFDM System

Aravinda Babu Tummala, Deergha Rao Korrai
{"title":"Performance Analysis of LDPC Coded Massive MIMO-OFDM System","authors":"Aravinda Babu Tummala, Deergha Rao Korrai","doi":"10.1109/INCET49848.2020.9154160","DOIUrl":null,"url":null,"abstract":"Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) wireless communication is well known in the literature. However, the problems occur in classical MIMO system can be overcome with large number of array antennas such systems, termed as Massive MIMO. But, the latency may be more for these systems using traditional equalizers such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). Hence, this paper proposes LDPC coded Massive MIMO OFDM system using Approximate Message Passing (AMP) equalizer. The performance of the proposed system is analysed through simulations. In this simulation, different transmit and receive antennas (64,128), (64,256), (64,512) and (64, 1024) and 16QAM are used. Finally, the performance of LDPC coded and uncoded massive MIMO OFDM using AMP equalizer is analyzed in comparison with ZF and MMSE equalizers using BER and latency as performance measures.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference for Emerging Technology (INCET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INCET49848.2020.9154160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) wireless communication is well known in the literature. However, the problems occur in classical MIMO system can be overcome with large number of array antennas such systems, termed as Massive MIMO. But, the latency may be more for these systems using traditional equalizers such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). Hence, this paper proposes LDPC coded Massive MIMO OFDM system using Approximate Message Passing (AMP) equalizer. The performance of the proposed system is analysed through simulations. In this simulation, different transmit and receive antennas (64,128), (64,256), (64,512) and (64, 1024) and 16QAM are used. Finally, the performance of LDPC coded and uncoded massive MIMO OFDM using AMP equalizer is analyzed in comparison with ZF and MMSE equalizers using BER and latency as performance measures.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
LDPC编码大规模MIMO-OFDM系统性能分析
多输入多输出正交频分复用(MIMO OFDM)无线通信在文献中得到了广泛的应用。然而,经典MIMO系统中出现的问题可以通过大量阵列天线来克服,这种系统被称为大规模MIMO。但是,对于使用传统均衡器(如零强制(ZF)和最小均方误差(MMSE))的系统来说,延迟可能更大。为此,本文提出了采用近似消息传递(AMP)均衡器的LDPC编码大规模MIMO OFDM系统。通过仿真分析了该系统的性能。在本仿真中,使用了不同的发射和接收天线(64,128)、(64,256)、(64,512)和(64,1024)和16QAM。最后,分析了使用AMP均衡器的LDPC编码和非编码大规模MIMO OFDM的性能,并与使用误码率和延迟作为性能指标的ZF和MMSE均衡器进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Investigation of DC Parameters of Double Gate Tunnel Field Effect Transistor (DG- TFET) for different Gate Dielectrics An Open-source Framework for Robust Portable Cellular Network Efficiency Comparison of Supervised and Unsupervised Classifier on Content Based Classification using Shape, Color, Texture Improved Divorce Prediction Using Machine learning- Particle Swarm Optimization (PSO) Machine Learning Based Synchrophasor Data Analysis for Islanding Detection
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1