ML and MAP Channel Estimation for Distributed OneWay Relay Networks with Orthogonal Training  

作　　者：YAO Chenhong ZHANG Shun PEI Changxing 

机构地区：[1]State Key Laboratory of Integrated Services Networks,Xidian University [2]Xian University of Architecture and Technology

出　　处：《China Communications》2015年第12期84-91,共8页中国通信（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant(61072067,61372076,61401332);the Postdoctoral Science Foundation of China (2014M552415);the Postdoctoral Science Special Foundation of China(2015T81006);the Programme of Introducing Talents of Discipline to Universities,China(Grant No. B08038)

摘　　要：In this letter,we investigate the individual channel estimation for the classical distributed-space-time-coding(DSTC) based one-way relay network(OWRN) under the superimposed training framework.Without resorting to the composite channel estimation,as did in traditional work,we directly estimate the individual channels from the maximum likelihood(ML) and the maximum a posteriori(MAP) estimators.We derive the closed-form ML estimators with the orthogonal training designing.Due to the complicated structure of the MAP in-channel estimator,we design an iterative gradient descent estimation process to find the optimal solutions.Numerical results are provided to corroborate our studies.In this letter,we investigate the individual channel estimation for the classical distributed-space-time-coding（DSTC） based one-way relay network（OWRN） under the superimposed training framework.Without resorting to the composite channel estimation,as did in traditional work,we directly estimate the individual channels from the maximum likelihood（ML） and the maximum a posteriori（MAP） estimators.We derive the closed-form ML estimators with the orthogonal training designing.Due to the complicated structure of the MAP in-channel estimator,we design an iterative gradient descent estimation process to find the optimal solutions.Numerical results are provided to corroborate our studies.

关 键 词：posteriori iterative estimator likelihood relay descent variance assumed iteration posed 

分 类 号：TN911.2[电子电信—通信与信息系统]