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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (10): 1987-1994    DOI: 10.3785/j.issn.1008-973X.2022.10.010
    
Modulation intelligent recognition based on phase fusion feature and residual network
Qun-ping LUO(),Min-jian ZHAO,Ming-min ZHAO*(),Zhi-zhen SU
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310013, China
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Abstract  

An intelligent signal modulation recognition algorithm based on phase constellation and trajectory clustering was proposed aiming at the problem that the common recognition relied on manually extracted empirical features and suffered from low precision. An ideal baseband signal was recovered by preprocessing the received signal, such as filtering and timing synchronization. Then the waveform data of the signal was transformed into a constellation diagram and vector trajectory diagram. The signal modulation recognition problem was transformed into a featured image classification problem by exploiting the techniques in deep learning (DL). The extracted features were input into a lightweight residual structure network with two parallel inputs. Then hierarchical learning and feature fusion training were performed to achieve the target modulation recognition. The simulation experiments showed that the recognition rates achieved by the feature fusion method outperformed those achieved by the methods based on high-order statistics, constellation diagrams and signal waveform. The recognition rate for seven types of modulation consisting of MPSK (M = 2, 4, 8), MQAM (M = 16, 64), and MAPSK (M = 16, 32) can reach 95.14% when the signal-to-noise ratio (SNR) is larger than 2 dB.

Key words： modulation recognition      constellation diagram      vector trajectory diagram      fusion training      residual network     
Received: 16 November 2021      Published: 25 October 2022
CLC:  TN 911  
Fund:  国防基础科研计划资助项目(JCKY2020210B021)
Corresponding Authors: Ming-min ZHAO     E-mail: luojing123@zju.edu.cn;zmmblack@zju.edu.cn
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Qun-ping LUO
Min-jian ZHAO
Ming-min ZHAO
Zhi-zhen SU
Cite this article:

Qun-ping LUO,Min-jian ZHAO,Ming-min ZHAO,Zhi-zhen SU. Modulation intelligent recognition based on phase fusion feature and residual network. Journal of ZheJiang University (Engineering Science), 2022, 56(10): 1987-1994.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.10.010     OR     https://www.zjujournals.com/eng/Y2022/V56/I10/1987


基于相位融合特征和残差网络的调制智能识别

针对调制识别依赖人工提取的经验特征、识别准确率低的问题，提出基于相位星座图和矢量轨迹图融合聚类的智能识别算法.通过对接收信号进行滤波、定时同步预处理，恢复出较理想的基带信号，将信号的波形数据转化为星座图和矢量轨迹图特征. 利用深度学习（DL）将调制识别转换成图像的分类问题，将所提取的特征通过2路并行输入的轻量级残差结构网络，开展分层学习和特征融合训练，完成对目标调制方式的识别. 仿真实验表明，基于融合特征的识别结果优于目前的基于高阶累积量、星座图和波形数据的识别结果，当信噪比（SNR）高于2 dB时，对MPSK（调制的阶数为 2、4、8）、MQAM（调制的阶数为16、64）、MAPSK（调制的阶数为16、32）这7类调制的识别率可以达到95.14%.


关键词： 调制识别,  星座图,  矢量轨迹图,  融合训练,  残差网络 
Fig.1 Process of modulation identification system
Fig.2 Modulation constellation diagram and vector diagram when SNR is 15 dB
Fig.3 Residual basic unit
Fig.4 CNN based on joint feature input
序号 组件 尺寸维度 描述
1
2
3
4
5
6
7
8
9
10 		输入
卷积层
卷积层
卷积层
残差单元
融合
卷积层
残差单元
全局平均
全连接 		3×224×224
32×112×112
32×112×112
32×112×112
32×112×112
64×112×112
128×56×56
256×28×28
256
7 		星座图和矢量图输入
32×Conv(3,3), Stride=2
32×Conv(3,3), Stride=1
32×Conv(3,3), Stride=1
Channel=32, Stride=1
Concat
128×Conv(3,3), Stride=2
Channel =256, Stride=2
Average pooling
输出
Tab.1 Details of network structure
名称 含义 数值
Learning rate
Batch size
Epoch
Optimizer
Weight decay
Momentum 		学习率
块包含的数据样本
最大迭代训练轮次
优化器
权重衰减
动量因子 		0.001
64
300
SGD
0.001
0.9
Tab.2 Hyperparameters of neural network
Fig.5 Average recognition performance
调制方式 本文的方法 基于星座图的卷积
网络识别方法 		基于高阶累积量的
KNN识别方法 		基于IQ数据的卷积
网络识别方法 		基于IQ数据的残差
网络识别方法
0 dB 5 dB 10 dB 0 dB 5 dB 10 dB 0 dB 5 dB 10 dB 0 dB 5 dB 10 dB 0 dB 5 dB 10 dB
BPSK 100 100 100 100 100 100 100 100 100 100 100 100 99.85 100 100
QPSK 98.33 100 100 98.33 100 100 100 100 100 44.25 100 100 81.80 100 100
8PSK 99.00 100 100 98.33 100 100 100 100 100 48.40 100 100 71.00 99.95 100
16APSK 77.67 100 100 77.67 100 100 68.50 75.83 82.33 35.60 83.40 99.95 47.05 97.65 100
32APSK 59.67 99.67 100 68.00 99.67 100 62.83 81.50 83.50 65.35 89.05 86.95 51.60 89.10 100
16QAM 100 100 100 76.00 100 100 84.17 90.50 95.33 17.65 60.85 100 38.90 86.05 100
64QAM 94.67 99.67 100 43.00 97.67 100 80.83 93.00 96.17 69.70 88.05 88.60 42.35 87.90 99.85
平均识别率 89.90 99.90 100 80.19 99.62 100 85.19 91.55 93.90 54.42 88.76 96.50 61.79 94.38 99.98
Tab.3 Modulation recognition rate at partial SNR %
Fig.6 Impacts of symbol length on recognition results
Fig.7 CNN based on single feature input (constellation diagram and vector diagram)
Fig.8 Recognition results based on different features
Fig.9 Impacts of residual module on recognition results
Fig.10 Scalability test
识别方法 $ {t_{{\rm{gen}}}} $/s $ {t_{{\text{train}}}} $/s $ {N_{{\text{net}}}} $
本文的方法
基于星座图的卷积网络识别
基于高阶累积量的KNN识别
基于IQ数据的卷积网络识别
基于IQ数据的残差网络识别 		0.758
0.168
0.0011
0.0023
0.0025 		372.4
239.33
22.668
34.8
49.56 		1070791
5967175
—
2748255
103337
Tab.4 Complexity of different algorithms
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