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

Abstract

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)

CLC Number:

TN911.7

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.

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References 20

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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

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

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