Wireless communication interference signal recognition model based on deep CNN

A complex neural network model based on deep convolutional neural networks (CNNs) is proposed to enhance recognition and suppression of interference signals in wireless communications. The model introduces a signal suppression network to address poor reception due to signal interference during transmission, which is a significant challenge in maintaining communication quality in increasingly complex wireless environments. Results show higher recognition accuracy for different interference signals at varied decibels. In complex networks, interference signal recognition accuracy is superior, with noise recognition accuracy surpassing other networks by 5% and 2%. The research method exhibits a lower bit error rate and 0.5 dB better amplitude suppression compared to traditional methods. The approach excels in interference signal identification and suppression, improving wireless communication signal recognition performance significantly. This advancement is crucial in ensuring the reliability and security of wireless communication systems, offering a novel solution to the growing challenges posed by interference in modern communication networks.