Space Time Block Coded Vector OFDM with ML Detection

B. K. Jeemon, Shahana T K
{"title":"Space Time Block Coded Vector OFDM with ML Detection","authors":"B. K. Jeemon, Shahana T K","doi":"10.1109/ICSCC51209.2021.9528236","DOIUrl":null,"url":null,"abstract":"Space time block coding (STBC) is a popular technique to improve diversity gain of conventional OFDM systems. Vector OFDM (VOFDM) is a transmission technology that exploits signal space dimension to reduce the effect of spectral nulls on OFDM subcarriers. Space time block coded vector OFDM (STBC VOFDM) tries to extract advantages of both these techniques, thereby improving the reliability of the communication system. This paper illustrates the characteristics of STBC VOFDM systems with maximum likelihood (ML) detection in an i.i.d (independent and identically distributed) multipath complex Rayleigh channel with D channel taps. The expression for diversity gain in STBC VOFDM for most vector blocks is derived as 2{min(M, D)}, where M denotes the number of elements in each vector block and D denotes the number of channel taps. It can be observed that the diversity order in STBC VOFDM has improved by a factor of 2 when compared with VOFDM.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCC51209.2021.9528236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Space time block coding (STBC) is a popular technique to improve diversity gain of conventional OFDM systems. Vector OFDM (VOFDM) is a transmission technology that exploits signal space dimension to reduce the effect of spectral nulls on OFDM subcarriers. Space time block coded vector OFDM (STBC VOFDM) tries to extract advantages of both these techniques, thereby improving the reliability of the communication system. This paper illustrates the characteristics of STBC VOFDM systems with maximum likelihood (ML) detection in an i.i.d (independent and identically distributed) multipath complex Rayleigh channel with D channel taps. The expression for diversity gain in STBC VOFDM for most vector blocks is derived as 2{min(M, D)}, where M denotes the number of elements in each vector block and D denotes the number of channel taps. It can be observed that the diversity order in STBC VOFDM has improved by a factor of 2 when compared with VOFDM.
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
时空块编码矢量OFDM与ML检测
空时分组编码(STBC)是提高传统OFDM系统分集增益的一种常用技术。矢量OFDM (VOFDM)是一种利用信号空间维度来减小频谱零值对OFDM子载波影响的传输技术。空时分组编码矢量OFDM (STBC VOFDM)试图提取这两种技术的优点,从而提高通信系统的可靠性。本文研究了具有独立同分布的多径复杂瑞利信道中具有最大似然检测的STBC VOFDM系统的特性。STBC VOFDM中大多数矢量块的分集增益表达式为2{min(M, D)},其中M表示每个矢量块中的元素数,D表示通道分接数。可以看出,STBC VOFDM的分集顺序比VOFDM提高了2倍。
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
FYEO : A Character Level Model For Lip Reading Parameter Dependencies and Optimization of True Random Number Generator (TRNG) using Genetic Algorithm (GA) Chaotic Time Series Prediction Model for Fractional-Order Duffing's Oscillator Segmentation of Brain Tumour in MR Images Using Modified Deep Learning Network Classification of Power Quality Disturbances in Emerging Power System with Distributed Generation Using Space Phasor Model and Normalized Cross Correlation
×
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