基于联合特征参数和一维CNN的MIMO-OFDM系统调制识别算法

doi:10.12305/j.issn.1001-506X.2023.03.32

系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (3): 902-912.doi: 10.12305/j.issn.1001-506X.2023.03.32

基于联合特征参数和一维CNN的MIMO-OFDM系统调制识别算法

汪锐, 张天骐, 安泽亮, 王雪怡, 方竹

重庆邮电大学通信与信息工程学院, 重庆 400065

收稿日期:2021-11-25 出版日期:2023-02-25 发布日期:2023-03-09
通讯作者: 汪锐
作者简介:汪锐(1998—), 女, 硕士研究生, 主要研究方向为通信信号盲处理、深度学习
张天骐(1971—), 男, 教授，博士研究生导师, 博士, 主要研究方向为通信信号的调制解调、盲处理、语音信号处理、神经网络实现, 以及现场可编程逻辑门阵列、超大规模集成电路实现
安泽亮(1993—), 男, 博士研究生, 主要研究方向为调制识别、机器学习和深度学习
王雪怡(1998—), 女, 硕士研究生, 主要研究方向为信道编码参数盲识别技术研究
方竹(1996—), 男, 硕士研究生, 主要研究方向为卫星扩频信号捕获
基金资助:
国家自然科学基金(61671095);国家自然科学基金(61702065);国家自然科学基金(61701067);国家自然科学基金(61771085);信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003);重庆市自然基金(cstc2021jcyj-msxmX0836);重庆市教育委员会科研项目(KJ1600427);重庆市教育委员会科研项目(KJ1600429)

Modulation recognition algorithm for MIMO-OFDM system based on joint characteristic parameters and one-dimensional CNN

Rui WANG, Tianqi ZHANG, Zeliang AN, Xueyi WANG, Zhu FANG

School of Communication and Information Engineering, Chongqing University of Posts and Telecom-munications, Chongqing 400065, China

Received:2021-11-25 Online:2023-02-25 Published:2023-03-09
Contact: Rui WANG

摘要/Abstract

摘要：

针对当前非协作通信中多输入多输出正交频分复用(multiple-input multiple-output orthogonal frequency division multiplexing, MIMO-OFDM)系统子载波的调制识别问题, 提出了一种基于一维卷积神经网络(one-dimensional convolutional neural network, 1D-CNN)的调制识别方法。首先, 利用特征矩阵的联合近似对角化(joint approximate diagonalization of eigenvalue matrix, JADE)算法从接收端的混合信号中恢复发送信号; 然后, 提取恢复信号的循环谱切片和四次方谱作为浅层特征; 最后, 利用1D-CNN对特征进行训练, 使用测试样本对所提出的调制识别方法进行仿真验证。仿真结果表明, 所提方法对MIMO-OFDM系统中的5种信号可以进行有效识别, 在信噪比为10 dB时的识别精度即可达到100%。

关键词: 多输入多输出正交频分复用, 调制识别, 循环谱, 四次方谱, 一维卷积神经网络

Abstract:

Aiming at the modulation identification of subcarriers in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in non cooperative communication, a modulation identification method based on one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, the joint approximate diagonalization of eigenvalue matrix (JADE) algorithm is used to recover the transmission signal from the mixed signal at the receiver. Then, the cyclic spectrum slice and quartic spectrum of the recovery signal are extracted as shallow features. Finally, the features are trained by 1D-CNN, and the proposed modulation recognition method is simulated and verified by test samples. Simulation results show that the proposed method can effectively identify five signals in MIMO-OFDM system, and the recognition accuracy can reach 100% when the signal-to-noise ratio is 10 dB.

Key words: multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), modulation identification, cyclic spectrum, quartic spectrum, one-dimensional convolutional neural network (1D-CNN)

中图分类号:

TN911.7

汪锐, 张天骐, 安泽亮, 王雪怡, 方竹. 基于联合特征参数和一维CNN的MIMO-OFDM系统调制识别算法[J]. 系统工程与电子技术, 2023, 45(3): 902-912.

Rui WANG, Tianqi ZHANG, Zeliang AN, Xueyi WANG, Zhu FANG. Modulation recognition algorithm for MIMO-OFDM system based on joint characteristic parameters and one-dimensional CNN[J]. Systems Engineering and Electronics, 2023, 45(3): 902-912.

图/表 14

图1

图2

图3

图4

图5

图6

表1

图7

图8

图9

图10

图11

图12

图13

参考文献 20

1	LIU X K , ZHAO C L , WANG P B , et al. Blind modulation classification algorithm based on machine learning for spatially correlated MIMO system[J]. IET Communications, 2017, 11 (7): 1000- 1007. doi: 10.1049/iet-com.2015.1222
2	CHOQUEUSE V , AZOU S , YAO K , et al. Blind modulation recognition for MIMO systems[J]. MTA Review, 2009, 19 (2): 183- 196.
3	赵雄文, 郭春霞, 李景春. 基于高阶累积量和循环谱的信号调制方式混合识别算法[J]. 电子与信息学报, 2016, 38 (3): 674- 680.
	ZHAO X W , GUO C X , LI J C . Mixed recognition algorithm for signal modulation schemes by high-order cumulants and cyclic spectrum[J]. Journal of Electronics & Information Technology, 2016, 38 (3): 674- 680.
4	PENG S L , JIANG H Y , WANG H X , et al. Modulation classification based on signal constellation diagrams and deep learning[J]. IEEE Trans.on Neural Networks and Learning Systems, 2018, 30 (3): 718- 727.
5	HO K C , PROKOPIW W , CHAN Y T . Modulation identification of digital signals by the wavelet transform[J]. IEEE Proceedings Radar, Sonar and Navigation, 2000, 147 (4): 169- 176. doi: 10.1049/ip-rsn:20000492
6	ZHANG L , LIU H , YANG X L , et al. Intelligent denoising aided deep learning modulation recognition with cyclic spectrum features for higher accuracy[J]. IEEE Trans.on Aerospace and Electronic Systems, 2021, 57 (6): 3749- 3757. doi: 10.1109/TAES.2021.3083406
7	ZERHOUNI K , AMHOUD E M , CHAFⅡ M . Filtered multicarrier waveforms classification: a deep learning-based approach[J]. IEEE Access, 2021, 9, 69426- 69438. doi: 10.1109/ACCESS.2021.3078252
8	ALI A K , ERÇELEBI E . Algorithm for automatic recognition of PSK and QAM with unique classifier based on features and threshold levels[J]. ISA Transactions, 2020, 102, 173- 192. doi: 10.1016/j.isatra.2020.03.002
9	O'SHEA T J, CORGAN J, CLANCY T C. Convolutional radio modulation recognition networks[C]//Proc. of the Engineering Applications of Neural Networks, 2016: 213-226.
10	O'SHEA T J , ROY T , CLANCY T C . Over-the-air deep learning based radio signal classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12 (1): 168- 179. doi: 10.1109/JSTSP.2018.2797022
11	WANG Y , WANG J , ZHANG W , et al. Deep learning-based cooperative automatic modulation classification method for MIMO systems[J]. IEEE Trans.on Vehicular Technology, 2020, 69 (4): 4575- 4579. doi: 10.1109/TVT.2020.2976942
12	ZHANG L , LIU H , YANG X L , et al. Intelligent denoising-aided deep learning modulation recognition with cyclic spectrum features for higher accuracy[J]. IEEE Trans.on Aerospace and Electronic Systems, 2021, 57 (6): 3749- 3757. doi: 10.1109/TAES.2021.3083406
13	查雄, 彭华, 秦鑫, 等. 基于多端卷积神经网络的调制识别方法[J]. 通信学报, 2019, 40 (11): 30- 37.
	ZHA X , PENG H , QIN X , et al. Modulation recognition method based on multi-inputs convolution neural network[J]. Journal on Communications, 2019, 40 (11): 30- 37.
14	ZHANG Z F , WANG C , GAN C Q , et al. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD[J]. IEEE Trans.on Signal and Information Processing over Networks, 2019, 5 (3): 469- 478. doi: 10.1109/TSIPN.2019.2900201
15	安泽亮, 张天骐, 马宝泽, 等. 基于一维CNN的多入多出OSTBC信号协作调制识别[J]. 通信学报, 2021, 42 (7): 84- 94.
	AN Z L , ZHANG T Q , MA B Z , et al. Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal[J]. Journal on Communications, 2021, 42 (7): 84- 94.
16	许宏吉, 刘琚, 谷波, 等. 空时分组码通信中的一类ICA盲检测方案[J]. 通信学报, 2007, 28 (6): 12- 19.
	XU H J , LIU J , GU B , et al. ICA based blind detection scheme in space-time block coding communications[J]. Journal on Communications, 2007, 28 (6): 12- 19.
17	张天骐, 范聪聪, 喻盛琪, 等. 基于JADE与特征提取的正交/非正交空时分组码盲识别[J]. 系统工程与电子技术, 2020, 42 (4): 933- 939.
	ZHANG T Q , FAN C C , YU S Q , et al. Blind recognition of orthogonal and non-orthogonal space-time block codes based on JADE and feature extraction[J]. Systems Engineering and Electronics, 2020, 42 (4): 933- 939.
18	张天骐, 范聪聪, 葛宛营, 等. 基于ICA和特征提取的MIMO信号调制识别算法[J]. 电子与信息学报, 2020, 42 (9): 2208- 2215.
	ZHANG T Q , FAN C C , GE W Y , et al. MIMO signal modulation recognition algorithm based on ICA and feature extraction[J]. Journal of Electronics & Information Technology, 2020, 42 (9): 2208- 2215.
19	RAMKUMAR B . Automatic modulation classification for cognitive radios using cyclic feature detection[J]. IEEE Circuits and Systems Magazine, 2009, 9 (2): 27- 45.
20	KIRANYAZ S , GASTLI A , BEN-BRAHIM L , et al. Real-time fault detection and identification for MMC using 1-D convolutional neural networks[J]. IEEE Trans.on Industrial Electronics, 2019, 66 (11): 8760- 8771.

名称	缺省值
优化器	Adam
批次大小(Batch)	128
初始学习率(lr)	0.001
迭代次数(epoch)	50
损失函数	categorical_crossentropy

[1]	宋波涛, 许广亮. 基于LSTM与1DCNN的导弹轨迹预测方法[J]. 系统工程与电子技术, 2023, 45(2): 504-512.
[2]	龚佩, 李天昀, 章昕亮, 寸陈韬. 利用联合特征参数的卫星单-混信号调制识别[J]. 系统工程与电子技术, 2023, 45(2): 589-596.
[3]	康颖, 赵治华, 吴灏, 李亚星, 孟进. 基于Deep SVDD的通信信号异常检测方法[J]. 系统工程与电子技术, 2022, 44(7): 2319-2328.
[4]	秦博伟, 蒋磊, 许华, 齐子森. 基于残差生成对抗网络的调制识别算法[J]. 系统工程与电子技术, 2022, 44(6): 2019-2026.
[5]	邵凯, 朱苗苗, 王光宇. 基于生成对抗与卷积神经网络的调制识别方法[J]. 系统工程与电子技术, 2022, 44(3): 1036-1043.
[6]	金涛, 王晓峰, 田润澜, 张歆东. 基于改进1DCNN+TCN的雷达辐射源快速识别方法[J]. 系统工程与电子技术, 2022, 44(2): 463-469.
[7]	张天骐, 白杨柳, 胡延平, 张晓艳. 交替二进制偏移载波信号的多参数盲估计[J]. 系统工程与电子技术, 2021, 43(9): 2665-2672.
[8]	史蕴豪, 许华, 郑万泽, 刘英辉. 基于集成学习与特征降维的小样本调制识别方法[J]. 系统工程与电子技术, 2021, 43(4): 1099-1109.
[9]	刘凯, 张斌, 黄青华. 基于TCNN-BiLSTM网络的调制识别算法[J]. 系统工程与电子技术, 2020, 42(8): 1841-1849.
[10]	刘凯, 赵梦伟, 黄青华. 基于近似熵的Multi-h CPM调制识别算法[J]. 系统工程与电子技术, 2020, 42(3): 698-703.
[11]	李晨, 杨俊安, 刘辉. 基于信息熵和GA-ELM的调制识别算法[J]. 系统工程与电子技术, 2020, 42(1): 223-229.
[12]	吴灏, 周亮, 李亚星, 郭宇, 孟进. 基于卷积神经网络和稀疏滤波的调制识别方法[J]. 系统工程与电子技术, 2019, 41(9): 2114-2121.
[13]	郝云飞, 刘章孟, 郭福成, 张敏. 基于生成对抗网络的信号调制方式的开集识别[J]. 系统工程与电子技术, 2019, 41(11): 2619-2624.
[14]	吴晓, 黎勇, 刘宏清. LDPC编码的MIMO-OFDM系统中的联合半盲均衡与解码研究[J]. 系统工程与电子技术, 2018, 40(8): 1866-1872.
[15]	谭晓衡, 褚国星, 张雪静, 杨扬. 基于高阶累积量和小波变换的调制识别算法[J]. 系统工程与电子技术, 2018, 40(1): 171-177.

基于联合特征参数和一维CNN的MIMO-OFDM系统调制识别算法

Modulation recognition algorithm for MIMO-OFDM system based on joint characteristic parameters and one-dimensional CNN

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 20

相关文章 15

编辑推荐

Metrics

本文评价