文章基本信息

标题：Channel Estimation Using DFT Based Automoly Classifying Neural Network
本地全文：下载
作者：Navneet Kaur ; Ramanpreet Kaur
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：6
出版社：S&S Publications
摘要：Channel Estimation refers to the evaluation of the performance of a channel through which the data is sent.MIMO OFDM(Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing) systems are quite effectivein terms of sending bulk data to the receiving end but it suffers with few problems also, like high amplification to noiseratio, etc. In such a scenario estimation techniques defines the ways of optimization against the data packets send andreceived through the channel. DFT is one of the effective techniques which can be used for the channel estimation. Thispaper focuses on the DFT based channel estimation technique and its comparison with other existing estimation techniqueslike MMSE, LS. This paper also includes a suggestion for the future enhancement of channel estimation techniques. Anoptimization technique like bacterial foraging optimization has been suggested here. The current paper focuses on theexisting scenarios of channel estimation techniques. The current paper also describes that the future aspects of channelestimation may include variation in MMSE techniques. This paper gives a review of the initialization of the neural networkthrough which the estimation can further be enhanced.
关键词：MIMO –OFDM; Channel Estimation; DFT; Neural Network; LS (Least Square); MMSE (Minimum Mean;Square)