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

作     者：Chen, Wenhao Ma, Lu Liang, Xuwen 

作者机构：Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Chinese Acad Sci Shanghai Engn Ctr Microsatellites Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Shanghai SpaceOK Aerosp Technol Co Ltd Shanghai 201802 Peoples R China 

出 版 物：《IEEE COMMUNICATIONS LETTERS》 (IEEE通讯快报)

年 卷 期：2018年第22卷第9期

页      面：1838-1841页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

基　　金：Shanghai Natural Science Foundation [shkw15zr01] 

主　　题：Maximum likelihood estimation multipath channels hidden Markov models Rhee-Clynn estimator expectation-maximization algorithm 

摘      要：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.