A Novel Approach Based on Generative Adversarial Network for Signal Enhancement in Wireless Communications

Abstract

Wireless communication signals are often affected by noise and interference in the channel during transmission, which makes it difficult for the receiver to analyze. The signal enhancement technology can suppress the noise and interference in the signal, so as to improve the communication quality. It is one of the main research directions of signal processing. Classical enhancement methods separate the signals through separable transform domain. Artificial construction of the corresponding separable transform domain requires prior information of noise and interference, but they have the characteristics of randomness. Further, these methods usually use high-level features and rely on statistics, so they can only deal with specific noise conditions. At present, deep learning is increasingly applied in the field of wireless communications due to its powerful feature extraction ability for large sample sets. In this paper, a communication signal enhancement model based on generative adversarial network (GAN) is proposed. Compared with classical methods, the signal is operated directly and the model is trained end-to-end. It can adapt to different noise conditions and avoid the above problems. An independent and invisible test set is used to evaluate several comparative methods. The experimental results confirm the effectiveness of the proposed model.