POWER ALLOCATION OF DATA DEPENDENT SUPERIMPOSED TRAINING

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

作者机构：Provincial Key Lab of Information Coding and Transmission Institute of Mobile Communications Southwest Jiaotong University Chengdu 610031 China 

出 版 物：《Journal of Electronics(China)》 (电子科学学刊（英文版）)

年 卷 期：2008年第25卷第5期

页      面：595-600页

学科分类：11[军事学] 0810[工学-信息与通信工程] 1105[军事学-军队指挥学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 081002[工学-信号与信息处理] 110503[军事学-军事通信学] 

基　　金：the National Natural Science Foundation of China(NSFC)(No.60472089) 

主　　题：Superimposed training Power allocation Channel estimation 

摘      要：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.