LDACS系统基于循环谱和残差神经网络的频谱感知方法

doi:10.12305/j.issn.1001-506X.2024.09.34

Abstract

Abstract:

To solve the problem that the available spectrum resources of L-band digital aeronautic communication system (LDACS) are limited and vulnerable to interference from high-power distance measuring equipment (DME) signals, a spectrum sensing method based on reduced dimension cyclic spectrum and residual neural network is proposed. Firstly, the cyclic spectrum characteristics of DME signal are analyzed theoretically. Then Fisher discriminant rate (FDR) is used to extract the vector with the highest cycle frequency energy, and the pre-processing features are enhanced by principal component analysis (PCA). Finally, the process of combining the cyclic spectral vector and convolutional neural network after data processing is given, and the effective detection of DME signal is achieved. Simulation results show that the method is not sensitive to noise, and the average detection probability is greater than 90% when the signal-to-noise ratio is no less than -15 dB. When the signal-to-noise ratio is not less than -14 dB, the detection probability is close to 100%.

Key words: L-band digital aeronautic communication system (LDACS), distance measuring equipment (DME), spectrum sensing, cyclic spectrum, residual neural network

CLC Number:

TN925

Lei WANG, Jin ZHANG, Qiuxuan YE. Spectrum sensing method based on cyclic spectrum and residual neural network in LDACS system[J]. Systems Engineering and Electronics, 2024, 46(9): 3231-3238.

Figures/Tables 11

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参数	参数值
采样频率/MHz	2.5
上采样因子	4
传输带宽/kHz	498.05
信道编码	卷积编码
FFT点数	64
有效子载波数	50
循环前缀点数	11
信道类型	AWGN信道
DME信号数目	1
DME载波偏置/kHz	+500、－500
信干比/dB	－3
脉冲间隔/μs	12

参数	ResNet-50	DenseNet-121	VGG-19	CNN-LSTM
训练时间/s	2 637.70	2 768.12	1 521.24	530.97
测试时间/s	8.351 9	8.714 7	4.865 2	1.320 8
准确度	0.952	0.946	0.910	0.766

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Spectrum sensing method based on cyclic spectrum and residual neural network in LDACS system

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