基于二维异步同相正交直方图的调制方式识别

doi:10.19665/j.issn1001-2400.20240312

Abstract

Abstract:

Automatic modulation recognition technology accurately identifies the modulation type of signals,making it a key technology in the field of signal processing.Traditional recognition methods suffer from low accuracy at low signal-to-noise ratios,and performance degradation or failure when dealing with signal frequency instability or asynchronous sampling.In this paper,we investigate modulation recognition technology based on deep learning for low-speed asynchronous sampled signals under channel conditions with varying signal-to-noise ratios and delays.We start by modeling low-speed asynchronous sampled signals and generating a two-dimensional asynchronous in-phase quadrature histogram using their in-phase and quadrature components.Subsequently,we employ a Radial Basis Function Neural Network to extract feature parameters from this two-dimensional image,thus achieving modulation type recognition for the input signal.Extensive computer simulations validate the proposed method’s accuracy in recognizing seven modulation types under the influence of additive white Gaussian noise.Experimental results demonstrate that,in the presence of additive white Gaussian noise in the channel model and with an input signal-to-noise ratio of 6 dB,the average recognition accuracy can exceed 95%.Comparative experiments further verify the effectiveness and robustness of the proposed approach.

Key words: modulation recognition, two-dimensional asynchronous in-phase quadrature histogram, deep learning, radial basis function neural network

CLC Number:

TN911.6

WAN Pengwu, HUI Xi, CHEN Dongrui, WU Bo. Modulation recognition based on the two-dimensional asynchronous in-phase quadrature histogram[J].Journal of Xidian University, 2024, 51(4): 78-90.

Figures/Tables 12

References 31

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参数	值	参数	值
符号速率/(symbols·s^-1)	4.1×10⁸	符号周期/s	2.44×10^-9
异步采样速率/(Mb·s^-1)	30	载波频率/Hz	4.1×10⁹
滤波器	滚降因子R=0.75	区间个数	40
信噪比/dB	2~16(步长为1)	信道	高斯白噪声信道
神经网络	径向基函数神经网络	总样本数	52 500
调制类型		BPSK、QPSK、8PSK、4QAM、16QAM、32QAM、64QAM

算法	训练时间
RBFNN	209.16
ELM	238.78
随机森林	309.94
SVM	336.23
CNN	655.17

Modulation recognition based on the two-dimensional asynchronous in-phase quadrature histogram

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