Latest Notice
WeChat Official Account
| Reference format: |
YIN Z, WANG C J, CHENG Z H, et al. An automatic modulation recognition algorithm based on convolutional neural networks with attention mechanism[J]. Chinese journal of radio science，2023，38（5）：773-779. （in Chinese）. DOI: 10.12265/j.cjors.2023120
|
Automatic modulation recognition (AMR) is an important link in fields such as communication recognition, electronic reconnaissance, and interference detection. In response to the problem of difficulty in accurately modulating and identifying interference sources in low signal-to-noise ratio environments, this paper constructs a modulation recognition model based on attention mechanism, sequential convolution-based attention model (SCAM), for modulating recognition of raw I/Q sequence signals. By introducing the attention mechanism in a one-dimensional convolutional neural network(CNN) model, SCAM can effectively extract feature information from the raw I/Q sequence signals under low signal-to-noise ratio conditions, and jointly extract multi-domain feature information through feature fusion, using the fused features for modulation recognition, thereby improving the accuracy of interference source type recognition. Through modulation recognition experiments under different signal-to-noise ratio environments on the open-source dataset RML2016.10a, compared with traditional convolutional neural network models, SCAM achieves higher modulation recognition accuracy.
| [1] |
XIE X, NI Y, PENG S, et al. Deep learning based automatic modulation classification for varying SNR environment[C]// The 28th Wireless and Optical Communications Conference (WOCC), 2019.
|
| [2] |
LIN Y, TU Y, DOU Z, et al. The application of deep learning in communication signal modulation recognition[C]// IEEE/CIC International Conference on Communications in China (ICCC), 2017.
|
| [3] |
TANG B, TU Y, ZHANG Z, et al. Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks[J]. IEEE access,2018,6:15713-15722. doi: 10.1109/ACCESS.2018.2815741
|
| [4] |
TU Y, LIN Y, WANG J, et al. Semi-supervised learning with generative adversarial networks on digital signal modulation classification[J]. Computers, materials and continua,2018,55(2):243-254.
|
| [5] |
WANG Y, LIU M, YANG J, et al. Data-driven deep learning for automatic modulation recognition in cognitive radios[J]. IEEE transactions on vehicular technology,2019,68(4):4074-4077. doi: 10.1109/TVT.2019.2900460
|
| [6] |
ZENG Y, ZHANG M, HAN F, et al. Spectrum analysis and convolutional neural network for automatic modulation recognition[J]. IEEE wireless communication letters,2019,8(3):929-932. doi: 10.1109/LWC.2019.2900247
|
| [7] |
O’SHEA T J, CORGAN J, CLANCY T C. Convolutional radio modulation recognition networks[C]// International Conference on Engineering Applications of Neural Networks(EANN), 2016.
|
| [8] |
WEST N E, O’SHEA T. Deep architectures for modulation recognition[C]// IEEE Dynamic Spectrum Access Networks (DySPAN), 2017.
|
| [9] |
YIN R, HUANG J, FEI Z. Short-time modulation classification of complex wireless communication signal based on deep neural network[C]// Asia Pacific Conference on Communications(APCC), 2018.
|
| [10] |
RAJENDRAN S, MEERT W, GIUSTINIANO D, et al. Deep learning models for wireless signal classification with distributed low-cost spectrum sensors[J]. IEEE transactions on cognitive communications and networking,2018,4(3):433-445. doi: 10.1109/TCCN.2018.2835460
|
| [11] |
LIN Y, TU Y, DOU Z. An improved neural network pruning technology for automatic modulation classification in edge devices[J]. IEEE transactions on vehicular technology,2020,69(5):5703-5706. doi: 10.1109/TVT.2020.2983143
|
| [12] |
何荣荣, 徐逸凡, 刘洁, 等. 基于软阈值深度学习的自动调制识别算法[J]. 电波科学学报,2022,37(3):465-470. doi: 10.12265/j.cjors.2021052
HE R R, XU Y F, LIU J, et al. A deep learning algorithm with soft threshold for automatic modulation recognition[J]. Chinese journal of radio science,2022,37(3):465-470. (in Chinese) doi: 10.12265/j.cjors.2021052
|
| [13] |
YIN Z, CHEN B, ZHEN W M, et al. The performance analysis of signal recognition using attention based CNN method[J]. IEEE access,2010,8:214915-214922.
|
| [14] |
张慧敏, 柴毅. 支持向量机多类分类的数字调制方式识别[J]. 重庆大学学报,2011,34(12):78-81. doi: 10.11835/j.issn.1000-582X.2011.12.013
ZHANG H M, CHAI Y. Digital modulation mode recognition based on multi-class classification of support vector machine[J]. Journal of Chongqing University,2011,34(12):78-81. (in Chinese) doi: 10.11835/j.issn.1000-582X.2011.12.013
|
| [15] |
杨发权, 李赞, 罗中良. 基于聚类与神经网络的无线通信联合调制识别新方法[J]. 中山大学学报(自然科学版),2015,54(2):24-29.
YANG F Q, LI Z, LUO Z L. A new spacific combination method of wireless communication modulation recognition based on clustering and neural network[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni,2015,54(2):24-29. (in Chinese)
|
| [16] |
HASSAN K, DAYOUB I, HAMOUDA W, et al. Automatic modulation recognition using wavelet transform and neural networks in wireless systems[J]. EURASIP journal on advances in signal processing,2010(1):532898. doi: 10.1155/2010/532898
|
| [17] |
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]// European Conference on Computer Vision(ECCV), 2018.
|
| [18] |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2016.
|
| [19] |
SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]// International Conference on Computer Vision(ICCV), 2017.
|
| [1] | TAN HaiDong, YANG JingJing, HUANG Ming. A study on the methodology for constructing radio maps utilizing differential convolution and attention mechanisms[J]. CHINESE JOURNAL OF RADIO SCIENCE. DOI: 10.12265/j.cjors.2024201 |
| [2] | WANG Chaoyu, YIN Ping. Attention-enhanced TCN-spatio temporal transformer model for ionospheric TEC prediction[J]. CHINESE JOURNAL OF RADIO SCIENCE. DOI: 10.12265/j.cjors.2024263 |
| [3] | LI Mingjie, CHEN Dongwei, WANG Tong, LIU Jinchao, LIU Weidong. Time delay estimation of partial discharge based on dual EEMD and reconstruction[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2024, 39(4): 760-768. DOI: 10.12265/j.cjors.2023223 |
| [4] | HE Xiusi, RUAN Fangming, XU Kai, YIN Lan, WANG Wenli. Analysis of discharge parameter prediction affected by electrode movement speed based on Attention-LSTM-XGBoost[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2024, 39(2): 287-295. DOI: 10.12265/j.cjors.2023154 |
| [5] | MAN Xiaojun, LI Huayu, MU Weiqing, GUO Lantu, ZHANG Peng, ZHANG Guilin, FENG Shihui. Inter-satellite spectrum prediction based method on particle swarm optimization GCN-LSTM neural network[J]. CHINESE JOURNAL OF RADIO SCIENCE. DOI: 10.12265/j.cjors.2024176 |
| [6] | LI Chuntang, LI Xu. Attention mechanism based inversions for ground penetrating radar image[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2023, 38(5): 825-834. DOI: 10.12265/j.cjors.2022175 |
| [7] | DING Zonghua, TANG Zhimei, DAI Liandong, WU Jian, XU Zhengwen. The range measurement of space object with high signal to noise ratio based on the incoherent scatter radar[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(6): 1081-1086. DOI: 10.13443/j.cjors.2016092702 |
| [8] | WANG Xiaofeng, TIAN Runlan, ZHANG Guoyi. A novel carrier frequency estimation method of phase shift keying signals in low SNR[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(1): 91-97. DOI: 10.13443/j.cjors.2015030101 |
| [9] | ZHU Wentao, SU Tao, YANG Tao, ZHENG Jibin, ZHU Kairan. Parameter estimation of linear frequency modulated continuous wave signal in low SNR[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2013, 28(6): 1153-1159. |
| [10] | FENG Zhi-hong, LAI Tao, ZHAO Yong-jun. Parameter estimation of STLFMCW signals in low SNR[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2012, 27(3): 520-525. |
| 1. |
张丹华，冯冀宁. 一种新的深度学习信号调制方式自动识别算法研究. 现代电子技术. 2025(12): 179-186 .
| |
| 2. |
刘静，李靖超，林军. 基于特征分布密度图的通信调制信号识别. 上海电机学院学报. 2024(03): 167-173 .
| |
| 3. |
牛瑞婷，严天峰，高锐，王映植. 低信噪比下基于深度学习TCNN-MobileNet的调制识别. 计算机工程. 2024(07): 204-215 .
| |
| 4. |
丁梦磊. 光学遥感图像中舰船识别方法研究. 舰船科学技术. 2024(16): 143-147 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: