Blind Identification Based on Expectation-Maximization Algorithm Coupled With Blocked Rhee–Glynn Smoothing Estimator

In this letter, we consider blind estimation of channel parameters over a frequency-selective channel. We use a blocked Rhee–Glynn smoothing estimator to derive E-step in the expectation-maximization (EM) algorithm. The proposed algorithm copes with the curse of dimensionality of a forward–backward algorithm; meanwhile, it is easy to parallelize, which is amenable to a modern computing hardware and speeds up the estimation of channel parameters. The experiment results show that the proposed algorithm is close to the Baum–Welch algorithm in terms of convergence of channel coefficients and outperforms the EM algorithm coupled with a joined two-filter smoothing algorithm in terms of convergence of channel coefficients and running time.