Channel Estimation for Relay-Based M2M Two-Way Communications Using Expectation-Maximization

The growing popularity of machine-to-machine (M2M) communications in wireless networks is driving the need to update the corresponding receiver technology based on the characteristics of M2M. In this paper, an expectation-maximization-based maximum likelihood cascaded channel estimation method is developed for relay-based M2M two-way communications. As the closed-form solution of maximum likelihood channel estimation does not exist, and the superimposed signal structure at the receiver is conducive to the expectation-maximization application, the expectation-maximization algorithm is utilized to provide the maximum likelihood solution in the presence of unobserved data through stable iterations. Even in the absence of the training sequence, the cascaded channel estimates are obtained through the expectation-maximization iterations. The Bayesian Cramér-Rao lower bounds are derived under random parameters for the channel estimation, and the simulation demonstrates the validity of the proposed studies.