通信学报 ›› 2019, Vol. 40 ›› Issue (7): 114-125.doi: 10.11959/j.issn.1000-436x.2019167
周鑫,何晓新,郑昌文
修回日期:
2019-04-11
出版日期:
2019-07-25
发布日期:
2019-07-30
作者简介:
周鑫(1986- ),男,河南项城人,博士,中国科学院软件研究所副研究员、硕士生导师,主要研究方向为认知无线网络、无线信道接入、射频机器学习等。|何晓新(1966- ),女,湖南衡阳人,中国科学院软件研究所研究员,主要研究方向为认知无线电、通信指挥系统等。|郑昌文(1969- ),男,湖北大冶人,博士,中国科学院软件研究所研究员、博士生导师,主要研究方向为计算机图形学、空间系统仿真、智能搜索等。
基金资助:
Xin ZHOU,Xiaoxin HE,Changwen ZHENG
Revised:
2019-04-11
Online:
2019-07-25
Published:
2019-07-30
Supported by:
摘要:
提出了一种利用图像深度学习解决无线电信号识别问题的技术思路。首先把无线电信号具象化为一张二维图片,将无线电信号识别问题转化为图像识别领域的目标检测问题;进而充分利用人工智能在图像识别领域的先进成果,提高无线电信号识别的智能化水平和复杂电磁环境下的识别能力。基于该思路,提出了一种基于图像深度学习的无线电信号识别算法——RadioImageDet 算法。实验结果表明,所提算法能有效识别无线电信号的波形类型和时/频坐标,在实地采集的12种、4 740个样本的数据集中,识别准确率达到86.04%,mAP值达到77.72,检测时间在中等配置的台式计算机上仅需33 ms,充分验证了所提思路的可行性和所提算法的有效性。
中图分类号:
周鑫,何晓新,郑昌文. 基于图像深度学习的无线电信号识别[J]. 通信学报, 2019, 40(7): 114-125.
Xin ZHOU,Xiaoxin HE,Changwen ZHENG. Radio signal recognition based on image deep learning[J]. Journal on Communications, 2019, 40(7): 114-125.
表2
RadioYOLO模型结构"
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 |
表3
北京地区三大运营商2G/3G/4G蜂窝下行频率分配"
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 |
表4
测试结果"
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 |
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