基于一维CNN的多入多出OSTBC信号协作调制识别

doi:10.11959/j.issn.1000-436x.2021142

Abstract

Abstract:

To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed.With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals.Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones.Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy.Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than-2 dB.

Key words: modulation recognition, MIMO-OSTBC, zero-forcing blind equalization, 1D-CNN, decision fusion

CLC Number:

TN911.7

Zeliang AN, Tianqi ZHANG, Baoze MA, Pan DENG, Yuqing XU. Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal[J]. Journal on Communications, 2021, 42(7): 84-94.

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References 15

[1]	SRIVASTAVA S , KUMAR M S , MISHRA A ,et al. Sparse doubly-selective channel estimation techniques for OSTBC MIMO-OFDM systems:a hierarchical Bayesian Kalman filter based approach[J]. IEEE Transactions on Communications, 2020,68(8): 4844-4858.
[2]	LARSSON E G , STOICA P , LI J . Orthogonal space-time block codes:maximum likelihood detection for unknown channels and unstructured interferences[J]. IEEE Transactions on Signal Processing, 2003,51(2): 362-372.
[3]	LUO M G , LI L P , TANG B . A blind modulation recognition algorithm suitable for MIMO-STBC systems[C]// International Conference on Computer and Information Technology. Piscataway:IEEE Press, 2012: 271-276.
[4]	. BAYER ? ， ?NER M . Joint space time block code and modulation classification for MIMO systems[J]. IEEE Wireless Communications Letters, 2017,6(1): 62-65.
[5]	TAYAKOUT H , DAYOUB I , GHANEM K ,et al. Automatic modulation classification for D-STBC cooperative relaying networks[J]. IEEE Wireless Communications Letters, 2018,7(5): 780-783.
[6]	MAREY M , DOBRE O A . Blind modulation classification for alamouti STBC system with transmission impairments[J]. IEEE Wireless Communications Letters, 2015,4(5): 521-524.
[7]	SHAH M H , DANG X Y . Low-complexity deep learning and RBFN architectures for modulation classification of space-time block-code (STBC)-MIMO systems[J]. Digital Signal Processing, 2020,99:102656.
[8]	CHOQUEUSE V , MANSOUR A , BUREL G ,et al. Blind channel estimation for STBC systems using higher-order statistics[J]. IEEE Transactions on Wireless Communications, 2011,10(2): 495-505.
[9]	ABRUDAN T E , ERIKSSON J , KOIVUNEN V . Steepest descent algorithms for optimization under unitary matrix constraint[J]. IEEE Transactions on Signal Processing, 2008,56(3): 1134-1147.
[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.
[11]	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.
[12]	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 Transactions on Industrial Electronics, 2019,66(11): 8760-8771.
[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]	PAPADIAS C B . Globally convergent blind source separation based on a multiuser kurtosis maximization criterion[J]. IEEE Transactions on Signal Processing, 2000,48(12): 3508-3519.
[15]	顾浙骐, 张志培 . 基于协作多点传输的非线性顽健预编码[J]. 通信学报, 2015,36(10): 140-148.
	GU Z Q , ZHANG Z P . Nonlinear robust precoding for coordinated multipoint transmission[J]. Journal on Communications, 2015,36(10): 140-148.

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均衡方法	先验信息需求	平均识别精度
	已知调制类型
ML盲均衡	空时码类型	91.46%
	盲信道估计值
ZF盲均衡	空时码类型	77.82%
	盲信道估计值
MUK盲均衡	发射天线数	61.24%

算法	内存消耗/KB	测试时间/ms
SAE-DNN算法	34	0.381
SVM算法	7 279	3.155
KNN算法	2 4261	1.178
1D-CNN算法	420	0.297

Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal

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