Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems

IF 2 Q3 TELECOMMUNICATIONS Journal of Computer Networks and Communications Pub Date : 2020-08-30 DOI:10.47277/ijcncs/8(8)1
S. H. Sackey, Michael Ansong, S. N. Kofie, Abdul Karim Armahy
{"title":"Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems","authors":"S. H. Sackey, Michael Ansong, S. N. Kofie, Abdul Karim Armahy","doi":"10.47277/ijcncs/8(8)1","DOIUrl":null,"url":null,"abstract":"The term Massive MIMO means, Massive multiple input multiple output also known as (large-scale antenna system, very large MIMO). Massive Multiple-Input-MultipleOutput (MIMO) is the major key technique for the future Fifth Generation (5G) of mobile wireless communication network due to its characteristics, elements and advantages. Massive MIMO will be comprised of five major elements; antennas, electronic components, network architectures, protocols and signal processing. We realize that precoding technique is a processing technique that utilizes Channel State Information Technique (CSIT) by operating on the signals before transmitting them. This technique varies base on the type of CSIT and performance criterion. Precoding technique is the last digital processing block at the transmitting side. In this paper, linear and non-linear Precoding technique was reviewed and we proposed two techniques under each that is Minimum Mean Square Error (MMSE), Block Diagonalization (BD), Tomlinson-Harashima (TH) and Dirty paper coding (DPC). Four Precoding techniques: MMSE, BD, DPC and TH were used in the studies to power consumption, energy efficiency and area throughput for single-cell and multi-cell scenarios. In comparing the proposed techniques, in terms of energy efficiency and area throughput, reuse factor (Reuse 4) performs better than other techniques when there is an imperfect CSI is used","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Networks and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47277/ijcncs/8(8)1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 1

Abstract

The term Massive MIMO means, Massive multiple input multiple output also known as (large-scale antenna system, very large MIMO). Massive Multiple-Input-MultipleOutput (MIMO) is the major key technique for the future Fifth Generation (5G) of mobile wireless communication network due to its characteristics, elements and advantages. Massive MIMO will be comprised of five major elements; antennas, electronic components, network architectures, protocols and signal processing. We realize that precoding technique is a processing technique that utilizes Channel State Information Technique (CSIT) by operating on the signals before transmitting them. This technique varies base on the type of CSIT and performance criterion. Precoding technique is the last digital processing block at the transmitting side. In this paper, linear and non-linear Precoding technique was reviewed and we proposed two techniques under each that is Minimum Mean Square Error (MMSE), Block Diagonalization (BD), Tomlinson-Harashima (TH) and Dirty paper coding (DPC). Four Precoding techniques: MMSE, BD, DPC and TH were used in the studies to power consumption, energy efficiency and area throughput for single-cell and multi-cell scenarios. In comparing the proposed techniques, in terms of energy efficiency and area throughput, reuse factor (Reuse 4) performs better than other techniques when there is an imperfect CSI is used
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
大规模MIMO系统的节能线性和非线性预编码器
大规模MIMO这个术语的意思是,大规模多输入多输出也被称为(大规模天线系统,超大型MIMO)。海量多输入多输出(Massive multi - input - multioutput, MIMO)由于其特点、构成要素和优势,是未来第五代(5G)移动无线通信网络的主要关键技术。大规模MIMO将由五大要素组成;天线,电子元件,网络架构,协议和信号处理。我们认识到预编码技术是利用信道状态信息技术(CSIT)在信号发送前对其进行操作的一种处理技术。这种技术根据CSIT的类型和性能标准而有所不同。预编码技术是发送端的最后一个数字处理模块。本文对线性和非线性预编码技术进行了综述,提出了最小均方误差(MMSE)、块对角化(BD)、Tomlinson-Harashima (TH)和脏纸编码(DPC)两种预编码技术。四种预编码技术:MMSE、BD、DPC和TH在研究中被用于单蜂窝和多蜂窝场景的功耗、能源效率和面积吞吐量。在比较所提出的技术时,就能源效率和面积吞吐量而言,当使用不完美的CSI时,重用因子(重用4)比其他技术表现更好
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
CiteScore
5.30
自引率
5.00%
发文量
18
审稿时长
15 weeks
期刊介绍: The Journal of Computer Networks and Communications publishes articles, both theoretical and practical, investigating computer networks and communications. Articles explore the architectures, protocols, and applications for networks across the full spectrum of sizes (LAN, PAN, MAN, WAN…) and uses (SAN, EPN, VPN…). Investigations related to topical areas of research are especially encouraged, including mobile and wireless networks, cloud and fog computing, the Internet of Things, and next generation technologies. Submission of original research, and focused review articles, is welcomed from both academic and commercial communities.
期刊最新文献
A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems Development of an AI-Enabled Q-Agent for Making Data Offloading Decisions in a Multi-RAT Wireless Network Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method Detecting Application-Level Associations Between IoT Devices Using a Modified Apriori Algorithm
×
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