Online blind equalization algorithm using extreme learning machine based on Kalman filter

被引：0
|
作者：
Yang L. [1 ]
Cheng L. [1 ]
Han Q. [1 ]
Zhao A. [1 ]
机构：
[1] School of Information Science and Engineering, Lanzhou University, Lanzhou
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 03期
关键词：
Blind equalization; Complex extreme learning machine; Kalman filter; Prediction method;
D O I：
10.12305/j.issn.1001-506X.2021.03.04
中图分类号：
学科分类号：
摘要：
For quadrature amplitude modulation (QAM) signals, a new neural network online blind equalization algorithm based on the Kalman filter (KF) is proposed under the blind equalization framework of the prediction method. For the purpose of minimizing the prediction error, this paper adopts the complex extreme learning machine(C-ELM) as the nonlinear prediction filter(PF) and sequentially updates the output weights of C-ELM using the KF. The amplitude of the signal is then adjusted by an automatic gain control device, and finally the phase rotation problem is corrected through a phase tuning factor. Simulation results show that the proposed algorithm achieves a good real-time equalization effect and has a faster convergence speed and lower steady mean square error. Moreover, the algorithm is suitable for blind equalization of the square, as well as the cross QAM signals. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：623 / 631
页数：8
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