基于多通道联合学习的自动调制识别网络

doi:10.3969/j.issn.1671-1122.2023.04.003

信息网络安全 ›› 2023, Vol. 23 ›› Issue (4): 20-29.doi: 10.3969/j.issn.1671-1122.2023.04.003

基于多通道联合学习的自动调制识别网络

赵彩丹¹, 陈璟乾¹, 吴志强²^,³()

1.厦门大学信息学院，厦门 361005
2.西藏大学信息科技学院，拉萨 850000
3.北京大学武汉人工智能研究院，武汉 430023

收稿日期:2022-11-17 出版日期:2023-04-10 发布日期:2023-04-18
通讯作者: 吴志强 E-mail:lightnesstibet@163.com
作者简介:赵彩丹（1974—），女，福建，副教授，博士，主要研究方向为无线信号处理、深度学习和无线网络安全|陈璟乾（1999—），男，福建，硕士研究生，主要研究方向为无线信号处理和深度学习|吴志强（1973—），男，北京，教授，博士，主要研究方向为无线信号处理、深度学习和调制编码。
基金资助:
国家自然科学基金(61971368);国家自然科学基金(61731012);国家自然科学基金委区域创新发展联合基金(U20A20162)

Automatic Modulation Recognition Algorithm Based on Multi-Channel Joint Learning

ZHAO Caidan¹, CHEN Jingqian¹, WU Zhiqiang²^,³()

1. School of Information Science and Technology, Xiamen University, Xiamen 361000, China
2. School of Information Science and Technology, Tibet University, Lhasa 850000, China
3. Peking University Institute for Artificial Intelligence, Beijing 100871, China

Received:2022-11-17 Online:2023-04-10 Published:2023-04-18
Contact: WU Zhiqiang E-mail:lightnesstibet@163.com

摘要/Abstract

摘要：

自动调制识别技术不仅可以提高频谱资源的利用率，而且是有效鉴别用户合法身份的方式之一。为进一步提高识别算法的性能，文章考虑幅度和相位特征之间的联系，提出了一种新的非对称多通道联合学习网络。该网络将幅度、相位以及两者的联合矩阵作为多通道输入端，在不改变参数量和计算速度的前提下利用非对称联合学习模块，较好地提取调制信号的幅度和相位中同质和异构特征，来实现自适应调制编码。实验结果表明，与其他深度学习网络相比，文章所提网络在基准开源数据集RadioML2016.10a和RadioML2016.10b上分别实现了91.73%和93.36%的识别精度。

关键词: 深度学习, 自动调制识别, 卷积神经网络, 联合学习

Abstract:

Automatic modulation recognition technology can not only effectively improve the utilization rate of spectrum resources, but is also an effective way to identify illegal users. To further improve the performance of the recognition algorithm, the paper proposed a new asymmetric multichannel joint learning network by considering the connection between amplitude and phase features. The network used the amplitude, phase and the joint matrix of both as multi-channel input to achieve adaptive modulation coding by better extracting homogeneous and heterogeneous features in the amplitude and phase of the modulated signal using an asymmetric joint learning module without changing the number of parameters and computational speed. The experiments results show that the network proposed in the article achieves the highest recognition accuracy of 91.73% and 93.36% on the benchmark open source datasets RadioML2016.10a and RadioML2016.10b, respectively.

Key words: deep learning, modulation recognition, convolutional neural networks, joint learning

中图分类号:

TP309

赵彩丹, 陈璟乾, 吴志强. 基于多通道联合学习的自动调制识别网络[J]. 信息网络安全, 2023, 23(4): 20-29.

ZHAO Caidan, CHEN Jingqian, WU Zhiqiang. Automatic Modulation Recognition Algorithm Based on Multi-Channel Joint Learning[J]. Netinfo Security, 2023, 23(4): 20-29.

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参考文献 23

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组成	描述	输出大小/张量
输入	AP 数值	2, 128
symmetric/ asymmetric-MJLNet		2, 128, 64
一维卷积Conv1	conv(64, 2*8, 1)	1, 128, 64
级联层Concat	Concat()	2, 128, 128
全局平均池化层AVGPOOL	GlobleAveragePooling2D()	1, 128
长短期记忆层LSTM * 2	LSTM(128) × 2	128
输出	Fc(11/10)	10/11

网络调制方式	SCRNN	1DCNN-PF	CNN2	CLDNN	MCLDNN	LSTM2	SCRNN	asymmetric-MJLNet
8PSK	84%	80%	77%	82%	86%	73%	84%	89%
AM-DSB	86%	99%	99%	87%	89%	93%	86%	90%
AM-SSB	97%	98%	95%	94%	92%	95%	97%	93%
BPSK	99%	98%	98%	99%	99%	94%	99%	99%
CPFSK	100%	100%	98%	96%	100%	99%	100%	100%
GFSK	99%	94%	94%	93%	99%	97%	99%	97%
PAM4	98%	98%	98%	97%	98%	96%	98%	98%
QAM16	52%	83%	40%	40%	81%	76%	52%	83%
QAM64	55%	71%	59%	63%	86%	48%	55%	81%
QPSK	82%	95%	40%	74%	96%	86%	82%	97%
WBFM	39%	29%	24%	43%	41%	33%	39%	38%

数据集	网络	识别率	训练时间/s	参数量/k	FLOPs/k
RadioML 2016.10a	CNN2	53.40%	17	2750	4648
	1DCNN-PF	57.63%	18	179	7514
	CLDNN	57.03%	14	164	6450
	MCLDNN	61.50%	31	406	1964
	LSTM2	56.79%	20	201	6082
	SCRNN	57.21%	11	40	5308
	asymmetric-MJLNet	61.70%	25	530	7238

基于多通道联合学习的自动调制识别网络

Automatic Modulation Recognition Algorithm Based on Multi-Channel Joint Learning

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