基于图像深度学习的无线电信号识别

doi:10.11959/j.issn.1000-436x.2019167

Abstract

Abstract:

A technical idea was innovatively proposed that uses image deep learning to solve the problem of radio signal recognition.First,the radio signal was transformed into a two-dimensional picture,and the radio signal recognition problem was transformed into the object detection problem in the field of image recognition.Then,the advanced achievements about image recognition were used to improve the intelligence and ability of radio signal recognition in complex electromagnetic environment.Based on the proposed idea,a novel radio signal recognition algorithm named RadioImageDet was proposed.The experimental results show that the algorithm can effectively identify the waveform types and time/frequency coordinates of radio signals.After training and testing on the self-collected data set with 12 types and 4 740 samples,the accuracy reaches 86.04% and the mAP value reaches 77.72,while the detection time is only 33 ms on the medium configured desktop computer.

Key words: radio signal recognition, deep learning, radio frequency machine learning, convolutional neural network, image object detection

CLC Number:

TN971

Xin ZHOU,Xiaoxin HE,Changwen ZHENG. Radio signal recognition based on image deep learning[J]. Journal on Communications, 2019, 40(7): 114-125.

Figures/Tables 15

References 37

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网络层类型	输入尺寸	参数	激活函数
conv层	128×2	3×1卷积核256个特征映射dropout 50%	ReLU
conv层	256×126×2	3×2卷积核80个特征映射dropout 50%	ReLU
dense层	9 920×1	256个神经元dropout 50%	ReLU
dense层	256×1	11个神经元	softmax

层名称	输入尺寸	尺寸/步进	卷积核数
conv1	512×512	3×3 / 1×1	32
maxpool1	512×512	2×2 / 2×2	—
conv2	256×256	3×3 / 1×1	64
maxpool2	256×256	2×2 / 2×2	—
conv3	128×128	3×3 / 1×1	128
conv4	128×128	1×1 / 1×1	64
conv5	128×128	3×3 / 1×1	128
maxpool3	128×128	2×2 / 2×2	—
conv6	64×64	3×3 / 1×1	256
conv7	64×64	1×1 / 1×1	128
conv8	64×64	3×3 / 1×1	256
maxpool4	64×64	2×2 / 2×2	—
conv9	32×32	3×3 / 1×1	512
conv10	32×32	1×1 / 1×1	256
conv11	32×32	3×3 / 1×1	512
conv12	32×32	1×1 / 1×1	256
conv13	32×32	3×3 / 1×1	512
maxpool5	32×32	2×2 / 2×2	—
conv14	16×16	3×3 / 1×1	1 024
conv15	16×16	1×1 / 1×1	512
conv16	16×16	3×3 / 1×1	1 024
conv17	16×16	1×1 / 1×1	512
conv18	16×16	3×3 / 1×1	1 024
conv19	16×16	3×3 / 1×1	1 024
conv20	16×16	3×3 / 1×1	1 024
conv21	32×32	1×1 / 1×1	64
reshape1	32×32	2×2	256
concatenate1	16×16	—	1 280
conv22	16×16	3×3 / 1×1	1 024
conv23	16×16	1×1 / 1×1	85

频率范围/MHz	信号名称
870～885	Tele2G_DL_CDMA
935～955	Mobi2G_DL_GSM900
955～960	Uni2G_DL_GSM900
1 805～1 820	Mobi2G_DL_GSM1800
1 840～1 860	Uni4G_DL_FDD1800
1 860～1 875	Tele4G_DL_FDD1800
1 885～1 905	Mobi4G_TDLTE_20M
1 905～1 915	Mobi4G_TDLTE_10M
2 010～2 025	Mobi3G_TDSCDMA
2 110～2 130	Tele4G_DL_FDD2100
2 130～2 135	Uni3G_DL_WCDMA
2 135～2 140	Uni3G_DL_WCDMA
2 140～2 155	Uni4G_DL_FDD2100
2 575～2 595	Mobi4G_TDLTE_20M
2 595～2 615	Mobi4G_TDLTE_20M
2 615～2 635	Mobi4G_TDLTE_20M

序号	信号名称	精准率	召回率	AP值
1	Tele2G_DL_CDMA	100%	98%	97.67
2	Mobi2G_DL_GSM900	100%	95%	95.35
3	Uni2G_DL_GSM900	100%	92%	92.31
4	Mobi2G_DL_GSM1800	100%	93%	93.02
5	Uni3G_DL_WCDMA	100%	100%	100
6	Mobi3G_TDSCDMA	30%	62%	45.76
7	Uni4G_DL_FDD1800	100%	26%	26.02
8	Uni4G_DL_FDD2100	94%	60%	58.50
9	Tele4G_DL_FDD1800	72%	98%	85.09
10	Tele4G_DL_FDD2100	54%	99%	55.02
11	Mobi4G_TDLTE_20M	94%	84%	83.91
12	Mobi4G_TDLTE_10M	93%	100%	99.92
平均	mAP	—	—	77.72

Radio signal recognition based on image deep learning

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