Adaptive virtual MIMO single cluster optimization in a small cell

T. Kanakis, Michael Opoku Agyeman, Anastasios G. Bakaoukas
{"title":"Adaptive virtual MIMO single cluster optimization in a small cell","authors":"T. Kanakis, Michael Opoku Agyeman, Anastasios G. Bakaoukas","doi":"10.1109/CONFLUENCE.2017.7943198","DOIUrl":null,"url":null,"abstract":"Adaptive Virtual MIMO optimized in a single cluster of small cells is shown in this paper to achieve near Shannon channel capacity when operating with partial or no Channel State Information. Although, access links have enormously increased in the recent years, the operational system complexity remains linear regardless of the number of access nodes in the system proposed. Adaptive Virtual MIMO optimized in a single cluster performs a theoretical information spectral efficiency, almost equal to that of the upper bounds of a typical mesh network, up to 43 bits/s/Hz at a SNR of 30dB while the BER performance remains impressively low hitting the 10−6 at an SNR of about 13 dB when the theoretical upper bound of an ideal small cell mesh network achieves the 10−6 at a SNR of 12.5 dB. In addition, in a sub-optimum channel condition, the channel capacity and BER performance of the proposed solution is shown to drastically delay saturation even for the very high SNR.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"28 1","pages":"474-478"},"PeriodicalIF":0.0000,"publicationDate":"2017-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONFLUENCE.2017.7943198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Adaptive Virtual MIMO optimized in a single cluster of small cells is shown in this paper to achieve near Shannon channel capacity when operating with partial or no Channel State Information. Although, access links have enormously increased in the recent years, the operational system complexity remains linear regardless of the number of access nodes in the system proposed. Adaptive Virtual MIMO optimized in a single cluster performs a theoretical information spectral efficiency, almost equal to that of the upper bounds of a typical mesh network, up to 43 bits/s/Hz at a SNR of 30dB while the BER performance remains impressively low hitting the 10−6 at an SNR of about 13 dB when the theoretical upper bound of an ideal small cell mesh network achieves the 10−6 at a SNR of 12.5 dB. In addition, in a sub-optimum channel condition, the channel capacity and BER performance of the proposed solution is shown to drastically delay saturation even for the very high SNR.
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
小小区自适应虚拟MIMO单簇优化
本文展示了在单个小单元簇中优化的自适应虚拟MIMO,在有部分或没有信道状态信息的情况下实现了接近香农信道的容量。尽管近年来接入链路大幅增加,但无论系统中提出多少个接入节点,操作系统的复杂性仍然是线性的。在单个集群中优化的自适应虚拟MIMO实现了理论的信息频谱效率,几乎等于典型网状网络的上界,在信噪比为30dB时高达43比特/秒/赫兹,而当理想的小蜂窝网状网络的理论上界在信噪比为12.5 dB时达到10−6时,误码率性能仍然很低,在信噪比约为13 dB时达到10−6。此外,在次优信道条件下,即使在非常高的信噪比下,所提出的解决方案的信道容量和误码率性能也会显着延迟饱和。
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
Hydrological Modelling to Inform Forest Management: Moving Beyond Equivalent Clearcut Area Enhanced feature mining and classifier models to predict customer churn for an E-retailer Towards the practical design of performance-aware resilient wireless NoC architectures Adaptive virtual MIMO single cluster optimization in a small cell Software effort estimation using machine learning techniques
×
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