• 学术论文 •

### 基于EM-SBL迭代的稀疏SIMO信道频域盲均衡算法

1. 信息工程大学信息系统工程学院, 河南郑州, 450001
• 收稿日期:2016-12-12 修回日期:2017-02-26 出版日期:2018-02-25
• 通讯作者:
• 张凯
• 作者简介:
• 于宏毅,男,1963年生,教授,研究方向为无线通信.E-mail:maxyucn@sohu.com
• 基金资助:
• 国家自然科学基金 (No.61501517）

### Blind Frequency-Domain Equalization for Sparse SIMO Channels Based on Iterative EM-SBL Algorithm

ZHANG Kai, YU Hong-yi, HU Yun-peng, SHEN Zhi-xiang

1. Institute of Information System Engineering, Information Engineering University, Zhengzhou, Henan 450001, China
• Received:2016-12-12 Revised:2017-02-26 Online:2018-02-25 Published:2018-02-25
• Corresponding author:
• ZHANG Kai

Abstract: Aiming at the equalization problem of sparse channels in single-input multiple-output (SIMO) systems, a new iterative frequency-domain equalization algorithm is proposed based on the maximum likelihood (ML) criterion. The equalization of multiple signals is first modeled as the maximum likelihood frequency-domain signal sequence estimation from incomplete observations, and approximately solved by means of the expectation-maximization (EM) algorithm in an iterative manner. Analytic expression of the equalization output is finally obtained in the form of weighted summation of each discrete-frequency signals. In each iteration, the proposed scheme alternates between equalization output update and channel posterior distribution update. During the later step, the inherent sparse nature of the channels is exploited by employing sparse promoting prior distributions. Then, the sparse Bayesian learning iterative inference method is applied to the proposed model in order to obtain joint conditional posterior distribution of the channel parameters. Simulation results show that the proposed scheme has a good convergence and steady-state performance, and approaches the steady-state symbol error rate (SER) with known channel parameters, at moderate and high signal-to-noise ratio (SNR) values.