{"title":"基于神经网络补偿器的MMSE接收机用于MIMO OFDM系统中HPA非线性","authors":"M. Dakhli, R. Zayani, R. Bouallègue","doi":"10.1109/MMS.2011.6068575","DOIUrl":null,"url":null,"abstract":"In this paper, we present a method based on Neural Network (NN) technique and accompanied with MMSE (Minimum Mean Square Error), which corrects at the receiver level, the Non-Linear (NL) distortions due to the HPA (High Power Amplifier). The neural network consists on a feed-forward Multi-Layer Perceptron (MLP) associated with Levenberg-Marquardt learning algorithm. The results show that the neural network compensator brings perceptible in a complete VBLAST MIMO OFDM (Vertical Bell Laboratories Layered Space-Time Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing) system running under a Rayleigh fading channel.","PeriodicalId":176786,"journal":{"name":"2011 11th Mediterranean Microwave Symposium (MMS)","volume":"4656 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Neural Network compensator based MMSE receiver for HPA nonlinearity in MIMO OFDM systems\",\"authors\":\"M. Dakhli, R. Zayani, R. Bouallègue\",\"doi\":\"10.1109/MMS.2011.6068575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a method based on Neural Network (NN) technique and accompanied with MMSE (Minimum Mean Square Error), which corrects at the receiver level, the Non-Linear (NL) distortions due to the HPA (High Power Amplifier). The neural network consists on a feed-forward Multi-Layer Perceptron (MLP) associated with Levenberg-Marquardt learning algorithm. The results show that the neural network compensator brings perceptible in a complete VBLAST MIMO OFDM (Vertical Bell Laboratories Layered Space-Time Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing) system running under a Rayleigh fading channel.\",\"PeriodicalId\":176786,\"journal\":{\"name\":\"2011 11th Mediterranean Microwave Symposium (MMS)\",\"volume\":\"4656 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 11th Mediterranean Microwave Symposium (MMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMS.2011.6068575\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 11th Mediterranean Microwave Symposium (MMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMS.2011.6068575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
摘要
在本文中,我们提出了一种基于神经网络(NN)技术并伴有最小均方误差(MMSE)的方法,该方法可以在接收机级校正由HPA(高功率放大器)引起的非线性(NL)失真。该神经网络由前馈多层感知器(MLP)和Levenberg-Marquardt学习算法组成。结果表明,在瑞利衰落信道下运行的VBLAST MIMO OFDM(垂直贝尔实验室分层空时多输入多输出正交频分复用)系统中,神经网络补偿器具有良好的可感知性。
Neural Network compensator based MMSE receiver for HPA nonlinearity in MIMO OFDM systems
In this paper, we present a method based on Neural Network (NN) technique and accompanied with MMSE (Minimum Mean Square Error), which corrects at the receiver level, the Non-Linear (NL) distortions due to the HPA (High Power Amplifier). The neural network consists on a feed-forward Multi-Layer Perceptron (MLP) associated with Levenberg-Marquardt learning algorithm. The results show that the neural network compensator brings perceptible in a complete VBLAST MIMO OFDM (Vertical Bell Laboratories Layered Space-Time Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing) system running under a Rayleigh fading channel.