基于信息熵和GA-ELM的调制识别算法

doi:10.3969/j.issn.1001-506X.2020.01.30

Abstract

Abstract:

In order to solve the problems of low recognition rate under low signal-to-noise ratio, slow training speed and few types of modulation in the current modulation recognition algorithms, this paper proposes a modulation recognition algorithm based on entropy feature and genetic algorithm-extreme learning machine (GA-ELM).Firstly, the four entropy characteristics of signals are extracted:Shannon entropy of singular spectrum, index entropy of singular spectrum, Shannon entropy of power spectrum and index entropy of power spectrum. Secondly, GA-ELM is used as the classifier. The simulation results show that the overall recognition rate of this algorithm is over 98% when the signal-to-noise ratio is more than 4 dB. At the same time, the algorithm has fast training speed, simple recognition system design and great application value.

Key words: modulation recognition, information entropy, extreme learning machine (ELM), genetic algorithm (GA)

CLC Number:

TN911.3

Chen LI, Jun'an YANG, Hui LIU. Modulation recognition algorithm based on information entropy and GA-ELM[J]. Systems Engineering and Electronics, 2020, 42(1): 223-229.

Figures/Tables 9

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References 27

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算法	ELM	BP	GA-ELM
时间	0.03	266.89	17.52

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Modulation recognition algorithm based on information entropy and GA-ELM

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调制类型	识别结果/dB
调制类型	-2	0	2	4	6	8	10	12	14	16
2ASK	80.50	84.50	89.50	97.50	98.00	98	100	100	100	100
2FSK	84	89.50	93.50	97	99	99	100	100	100	100
2PSK	91.50	92	94	96	97.50	98	99	100	100	100
4ASK	92	93.50	94.50	95	94.50	99.50	97	100	100	100
4FSK	99.50	100	100	100	100	100	100	100	100	100
4PSK	89	93	95.50	99	100	99	99.50	100	100	100
16QAM	90	95	98	100	99.50	98.50	100	100	100	100
32QAM	91	97	99	99.50	100	100	100	100	100	100
AM	99.50	100	100	100	100	100	100	100	100	100
FM	100	100	100	100	100	100	100	100	100	100
PM	100	100	100	100	100	100	100	100	100	100