基于卡尔曼滤波的极限学习机在线盲均衡算法

doi:10.12305/j.issn.1001-506X.2021.03.04

Abstract

Abstract:

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.

Key words: blind equalization, prediction method, complex extreme learning machine, Kalman filter

CLC Number:

Ling YANG, Li CHENG, Qin HAN, Aonan ZHAO. Online blind equalization algorithm using extreme learning machine based on Kalman filter[J]. Systems Engineering and Electronics, 2021, 43(3): 623-630.

Figures/Tables 13

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References 25

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隐含层节点数	SNR/dB
隐含层节点数	10	15	20	25	30	35
5	0.6	-4.0	-8.2	-11.4	-12.8	-13.5
15	0.5	-3.9	-7.9	-11.1	-12.5	-13.1
50	0.7	-3.8	-7.8	-11.0	-11.9	-12.5

隐含层节点数	迭代次数/次
5	2 500~2 600
15	2 500~2 600
50	2 500~2 600

隐层激活函数	SNR/dB
隐层激活函数	10	15	20	25	30	35
arctan	0.7	-4.0	-8.1	-11.4	-12.9	-13.5
sin	0.8	-4.0	-8.1	-11.3	-12.8	-13.3
tanh	0.6	-3.8	-8.0	-11.2	-12.5	-13.2

隐层激活函数g(·)	迭代次数/次
arctan	2 500~2 600
sin	3 100~3 200
tanh	2 500~2 600

输出函数f_readout(·)	SNR/dB
输出函数f_readout(·)	10	15	20	25	30	35
线性	0.6	-4.0	-8.2	-11.4	-12.9	-13.5
非线性	0.5	-7.8	-19.0	-25.5	-28.5	-28.8

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

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算法	计算复杂度
C-ELM-KF-PF	l_ELM³+3l_ELM²+6l_ELM
MLP-PF	14 l_MLP
Linear-PF	14L_p

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