基于残差生成对抗网络的调制识别算法

doi:10.12305/j.issn.1001-506X.2022.06.30

Abstract

Abstract:

Aiming at the problem of low accuracy of communication signal recognition and difficult training of network in few-shot learning, a modulation recognition algorithm based on the residual generative adversarial network is proposed. Firstly, this algorithm designs a new residual block unit using Leakyrelu as the activation function of the hidden layer, which can also calculate the gradient for the negative input data. Then, the residual network composed of the new residual units and the convolutional neural network are used as the basic network structure of this proposed algorithm. Moreover, we apply the asymmetric small convolutional kernel with a convolutional step of 1 to better extract the edge feature information of the signal. Finally, Dropout is adopted to replace the pooling operation, and Adam gradient optimization algorithm is selected to complete the network training in an alternate iteration mode. Experimental results show that under the condition of a few-shot learning, the recognition accuracy of the residual generation adversarial network algorithm for 10 modulated signals can reach 91% when the signal to noise ratio (SNR) is higher than 0 dB, which verifies the effectiveness of the proposed method.

Key words: residual network (Resnet), generative adversarial network, modulation recognition, few-shot

CLC Number:

TB975

Bowei QIN, Lei JIANG, Hua XU, Zisen QI. Modulation recognition algorithm based on residual generation adversarial network[J]. Systems Engineering and Electronics, 2022, 44(6): 2019-2026.

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References 22

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参数	数值
调制样式	8PSK, AM-DSB, BPSK, GFSK CPFSK, PAM4, QAM16, QAM64, QPSK, WBFM
样本维度	(2, 128)
样本数量	86 400
信噪比	-4~18 dB (每2 dB一个间隔)

参数	批次
参数	32	64	128	256	512
第一衰减率	0.4	0.5	0.6	0.5	0.4
Dropout	0.3	0.05	0.15	0.3	0.2
识别准确率	0.918	0.980	0.952	0.934	0.928

网络	参数
网络	网络参数	运行时间/s	分类准确率
CLDNN^[14]	3 616 348	94	0.823
AUCNN^[15]	2 861 583	85	0.887
Resnet-WSMF^[17]	3 522 641	89	0.934
ACGAN^[18]	G: 320 152 D: 268 993	49	0.804
Res-GAN(本文)	G: 310 721 D: 778 291	68	0.980

网络	标签样本量
网络	540	600	660	720	780	1 800	3 600	7 200	15 000	30 000	60 000
CLDNN^[14]	0.192	0.223	0.261	0.285	0.311	0.355	0.441	0.557	0.632	0.774	0.785
AUCNN^[15]	0.221	0.248	0.355	0.374	0.391	0.425	0.504	0.663	0.724	0.827	0.832
Resnet-WSMF^[17]	0.191	0.198	0.254	0.284	0.316	0.345	0.397	0.445	0.664	0.832	0.895
ACGAN^[18]	0.233	0.243	0.265	0.287	0.328	0.391	0.462	0.683	0.764	0.825	0.863
Res-GAN	0.849	0.897	0.980	0.979	0.978	0.979	0.975	0.980	0.979	0.977	0.978

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Modulation recognition algorithm based on residual generation adversarial network

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