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 th...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.展开更多
基金supported by the National Natural Science Foundation of China under Grant(61072067,61372076,61401332)the Postdoctoral Science Foundation of China (2014M552415)+1 种基金the Postdoctoral Science Special Foundation of China(2015T81006)the Programme of Introducing Talents of Discipline to Universities,China(Grant No. B08038)
文摘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.
