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

作     者：YAO Chenhong ZHANG Shun PEI Changxing 

作者机构：State Key Laboratory of Integrated Services NetworksXidian University Xian University of Architecture and Technology 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2015年第12卷第12期

页      面：84-91页

核心收录：

学科分类：07[理学] 08[工学] 070104[理学-应用数学] 081101[工学-控制理论与控制工程] 0701[理学-数学] 0811[工学-控制科学与工程] 

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) 

主　　题：posteriori iterative estimator likelihood relay descent variance assumed iteration posed 

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 *** 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) *** derive the closed-form ML estimators with the orthogonal training *** to the complicated structure of the MAP in-channel estimator,we design an iterative gradient descent estimation process to find the optimal *** results are provided to corroborate our studies.