基于群体智能的MIMO系统MMSE频域均衡

Q3 Engineering
D. Diana, R. Hema
{"title":"基于群体智能的MIMO系统MMSE频域均衡","authors":"D. Diana, R. Hema","doi":"10.37936/ecti-eec.2023212.249824","DOIUrl":null,"url":null,"abstract":"The automatic upgradation of equalizer weights in channel equalization demands a low-complexity, highly accurate estimation of recovery at the minimum possible time. The low-complexity frequency domain equalization improves the minimum mean square error (MMSE) of the equalization process. Adding the superiority of particle swarm optimization (PSO) to the equalizer coefficient selection process enhances the MMSE. This work proposes frequency-domain channel equalization along with a modified PSO (MPSO) as an adaptive algorithm for equalizer weight selection in MIMO systems. The simulation results validate the performance with the time domain linear and decision feedback equalizer structures for BPSK and QAM systems. The parameters are carefully selected by analyzing MMSE thoroughly under timevarying channel conditions.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"14 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Swarm Intelligence Based MMSE Frequency Domain Equalization for MIMO Systems\",\"authors\":\"D. Diana, R. Hema\",\"doi\":\"10.37936/ecti-eec.2023212.249824\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The automatic upgradation of equalizer weights in channel equalization demands a low-complexity, highly accurate estimation of recovery at the minimum possible time. The low-complexity frequency domain equalization improves the minimum mean square error (MMSE) of the equalization process. Adding the superiority of particle swarm optimization (PSO) to the equalizer coefficient selection process enhances the MMSE. This work proposes frequency-domain channel equalization along with a modified PSO (MPSO) as an adaptive algorithm for equalizer weight selection in MIMO systems. The simulation results validate the performance with the time domain linear and decision feedback equalizer structures for BPSK and QAM systems. The parameters are carefully selected by analyzing MMSE thoroughly under timevarying channel conditions.\",\"PeriodicalId\":38808,\"journal\":{\"name\":\"Transactions on Electrical Engineering, Electronics, and Communications\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions on Electrical Engineering, Electronics, and Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37936/ecti-eec.2023212.249824\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Electrical Engineering, Electronics, and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37936/ecti-eec.2023212.249824","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

摘要

在信道均衡中,均衡器权重的自动升级要求在尽可能短的时间内对恢复进行低复杂度、高精度的估计。低复杂度频域均衡提高了均衡过程的最小均方误差(MMSE)。在均衡器系数选择过程中加入粒子群算法的优越性,提高了最小均方误差(MMSE)。这项工作提出了频域信道均衡以及改进的PSO (MPSO)作为MIMO系统中均衡器权重选择的自适应算法。仿真结果验证了时域线性和决策反馈均衡器结构对BPSK和QAM系统的性能。通过深入分析时变信道条件下的MMSE,仔细选择参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Swarm Intelligence Based MMSE Frequency Domain Equalization for MIMO Systems
The automatic upgradation of equalizer weights in channel equalization demands a low-complexity, highly accurate estimation of recovery at the minimum possible time. The low-complexity frequency domain equalization improves the minimum mean square error (MMSE) of the equalization process. Adding the superiority of particle swarm optimization (PSO) to the equalizer coefficient selection process enhances the MMSE. This work proposes frequency-domain channel equalization along with a modified PSO (MPSO) as an adaptive algorithm for equalizer weight selection in MIMO systems. The simulation results validate the performance with the time domain linear and decision feedback equalizer structures for BPSK and QAM systems. The parameters are carefully selected by analyzing MMSE thoroughly under timevarying channel conditions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Transactions on Electrical Engineering, Electronics, and Communications
Transactions on Electrical Engineering, Electronics, and Communications Engineering-Electrical and Electronic Engineering
CiteScore
1.60
自引率
0.00%
发文量
45
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信