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superimposed training for channel estimation of OFDM modulated amplify-and-forward relay networks

Science China(Information Sciences) 2013年第10期56卷 55-66页

作者： ZHANG Han PAN DaRu CUI HaiXia GAO FeiFei School of Physics and Telecommunication Engineering South China Normal University Tsinghua National Laboratory for Information Science and Technology National Mobile Communications Research Laboratory Southeast University

This paper is concerned with the problem of channel estimation for amplify-and-forward(AF)cooperative relay networks with orthogonal frequency division multiplexing(OFDM)*** algorithm is based on both the least square(LS)and minimum mean square error(MMSE)technique with a superimposed training ***,both the source and relay superimpose their own training signal onto data stream prior to transmission so as to estimate the separate channel state information of the source to relay link and the relay to destination *** also present the performance analysis and derive the approximated closed-form expressions for the MSE of separate channel estimation of source to relay link and the relay to destination link,respectively,from which we compute the optimal training signal as well as the relay power-amplifcation *** further improve the performance of channel estimation,we adopt a weighted average process to enhance the estimation performance over multiple OFDM blocks,from which we compute the optimal tracking *** results are provided to corroborate the proposed scheme.

关键词： channel estimation amplify-and-forward relay network OFDM superimposed training

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Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training

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Science China(Information Sciences) 2011年第7期54卷 1456-1470页

作者： ZHANG Han 1,DAI XianHua 2 & Pan DaRu 1 1 School of Physics and Telecommunication Engineering,South China Normal University,Guangzhou 510006,China 2 Department of Information Science and Technology,SUN Yat-sen University,Guangzhou 510026,China 1. School of Physics and Telecommunication Engineering South China Normal University Guangzhou 510006 China2. Department of Information Science and Technology SUN Yat-sen University Guangzhou 510026 China

We address the problem of estimating the linearly time-varying (LTV) channel of orthogonal frequency division multiplexing (OFDM)/multiple-input multiple-output (MIMO) systems using superimposed training (ST).The LTV channel is modeled by truncated discrete Fourier *** on this model,a two-step approach is adopted to estimate the LTV channel over multiple OFDM *** also present performance analysis of the channel estimation and derive a closed-form expression for the channel estimation *** is shown that the estimation variances,unlike that of the conventional ST-based schemes,approach to a fixed lower-bound as the training length increases,which is directly proportional to information-pilot power *** wireless communication systems with a limited transmission power,we optimize the ST power allocation by maximizing the lower bound of the average channel *** results show that the proposed approach outperforms the frequency-division multiplexed training schemes.

关键词： OFDM/MIMO systems linearly time-varying channel estimation superimposed training training power allocation

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POWER ALLOCATION OF DATA DEPENDENT superimposed training

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Journal of Electronics(China) 2008年第5期25卷 595-600页

作者： Wang Ping Yuan Weina Fan Pingzhi Provincial Key Lab of Information Coding and Transmission Institute of Mobile Communications Southwest Jiaotong University Chengdu 610031 China

Data Dependent superimposed training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel *** DDST,however,the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence,which is the motivation of this *** general,for DDST,there is a tradeoff between the channel estimation accuracy and the data detection reliability,i.e.,the more accurate the channel estimation,the more reliable the data detection;on the other hand,the more accurate the channel estimation,the more demanding on the power consumption of training sequence,which in turn leads to the less reliable data *** this paper,the relationship between the Signal-to-Noise Ratio(SNR) of the data detector and the training sequence power is *** optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the *** and simulation results show that for a fixed transmit power,the SNR and the Symbol Error Rate(SER) of detector vary nonlinearly with the increasing of training sequence power,and there exists an optimal power ratio,which accords with the derived optimal power ratio,among the data and training sequence.

关键词： superimposed training Power allocation Channel estimation

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Sparse underwater acoustic OFDM channel estimation based on superimposed training

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Journal of Marine Science and Application 2009年第1期8卷 65-70页

作者：赵俊义孟维晓贾世楼哈尔滨工业大学通信技术研究所黑龙江哈尔滨150001

A superimposed training （ST） based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing （OFDM） channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel＇s properties include large channel dimensions and a sparse structure, so a matching pursuit （MP） algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise （AWGN） to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.

关键词： channel estimation superimposed training sparse underwater acoustic channel

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