A CPM signal denoising method based on attention network

Abstract

Cognitive communication countermeasure system utilizes artificial intelligence technology to quickly realize electromagnetic dynamic perception and electronic jamming strategy generation. In the complex electromagnetic environment of the modern battlefield, continuous phase modulation (CPM) signals are getting more and more attention due to high spectral efficiency and power efficiency. CPM signal denoising processing helps to improve electromagnetic dynamic perception performance. In this paper, a novel model, namely attentional denoising autoencoder (ADE), is proposed with enhanced signal denoising by introducing self-attentional mechanism into the autoencoder. The proposed method divides the one-dimensional communication signal sequence into fixed-size signal patches satisfying the same modulation law, and then utilizes the parallel computing of the self-attention mechanism to model the dependencies between the signal patches, and finally average pooling is used to synthesize the information of each signal patch to reconstruct the signal. The simulation results demonstrate that the proposed model is superior to other methods in terms of the denoising effect, and has a high degree of waveform recovery, which is helpful for the subsequent perception and processing of CPM signals.