5G mobile communication interference signal detection based on convolutional neural network
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.
Convolutional Neural Network5G mobile communicationinterference signalwavelet thresh-old denoisingtime-frequency map
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