A high‐efficient information extraction mechanism based on complex convolution for wireless signal recognition

Abstract

Abstract In the integration of terrestrial and non‐terrestrial networks, massive access to wireless signals poses a significant threat to communication systems. However, existing signal recognition models cannot accurately identify signals with low signal‐to‐noise ratios (SNRs). To alleviate this issue, this paper proposes a high‐efficient information extraction mechanism based on complex convolution. Specifically, complex convolution and complex max‐pooling operations have been integrated into a real‐value network to efficiently acquire anti‐noise information. The extracted features contain both in‐phase and quadrature (IQ) structure information and complex relationship information. Experiments on the public RML2016.04C dataset indicated that the model exhibits rapid convergence and superior noise‐robustness under low SNRs. When SNR =0 dB, the model outperforms the suboptimal model by 4.05%. Furthermore, our model demonstrates excellent generalization performance at high SNRs.

Keywords

• cognitive radio
• feature extraction
• neural net architecture
• radiofrequency identification