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

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.