Based on the problem that the interference in 5G mobile communication signal affects the com-munication performance,a 5 G mobile communication interference signal detection method based on convo-lutional neural network is proposed.Combining the advantages of soft and hard threshold functions,the wavelet threshold function is improved.The 5G mobile communication signal containing interference signals is denoised.The time-frequency map is constructed by Fourier transform and normalized,and the improved convolutional neural network is trained by using the random gradient descent algorithm with momentum.The normalized time-frequency map is input,and the interference signal detection result is finally output.The experiment results show that the proposed method has stronger denoising ability,smaller training loss,high-er training accuracy and detection accuracy.
关键词
卷积神经网络/5G移动通信/干扰信号/小波阈值去噪/时频图
Key words
Convolutional Neural Network/5G mobile communication/interference signal/wavelet thresh-old denoising/time-frequency map
万方数据