ML and MAP Channel Estimation for Distributed One-Way 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.