Interference Response Prediction of Receiver Based on Wavelet Transform and a Temporal Convolution Network

Abstract

In order to improve the prediction performance of existing methods amidst multi modulation coupling interference in complex electromagnetic environments, this paper introduces a novel approach that integrates wavelet transform with a temporal convolutional network. The model begins with a data preprocessing stage, where wavelet transform decomposes the original signal into various scales. This step generates scale coefficients across different frequency categories, effectively reducing the signal length. To enhance the model’s ability to capture long-term dependencies in time series data, temporal convolutional networks are employed for feature extraction. Moreover, the model’s performance is further refined by incorporating an attention mechanism-driven feature fusion strategy. This strategy methodically combines high and low frequency features along with local and global characteristics. The model’s efficacy is validated using a custom MATLAB dataset, with simulation results confirming a significant improvement in prediction accuracy.